Engineering Innovations Volume 14

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Volume 14 of
Engineering Innovations
ISSN print 2813-0995
ISSN web 2813-1002

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Table of Contents

Tunable Hydrophobicity and Photoluminescence in Polystyrene-Calcium Carbonate Composites with Citric Acid-Induced Porosity

Ayodunmomi Esther Olowofoyeku, Ademola Kabiru Aremu, Abel Olajide Olorunnisola, Ayobamiji Emmanuel Olowofoyeku, Jesus Roberto Villegas Mendez, Daniel Gbenga Adekanmi

Study of Kevlar Aramid Fiber in Improving Concrete Performance and Mitigating Pathologies

Danielle Ferreira dos Santos, Amanda De Queiroz Mendes

Smart Architecture: Case Studies and Breakthrough Applications of Advanced Materials in Buildings

Valentina Frighi

Study of Solar and Wind Energy Potential for Power Generation in Pulo Aceh

Agus Adria, Ahmad Syuhada, Tarmizi Tarmizi, Ira Devi Sara

Challenges in Water Energy Development for Energy Security in Aceh

Razali Thaib, Zulfan Zulfan, Ichsan Setiawan, Ayana Rizki

Analysis of the Effect of Discharge Needs on Pesangan Hydropower Plant in Central Aceh Regency

Salsabila Ashmalia Putri, Fadhliani Umar, Teuku Mudi Hafli, Adzuha Desmi, Nanda Savira Ersa

IoT Based Environmental and Water Monitoring System for Sterilization Units in Honduras

Carlos Matute Molina, Fernando Calderon Perdomo, Fernanda de Lourdes Cáceres Lagos

Biomimetic Robot with Peristaltic Locomotion Integrating 3D Printing and IoT Technologies

José L. Sandoval-Ferrufino, Bryan E. Alvarado-Orellana, Julio Valentin Fajardo

Implementation of an IOT Sensor Network and Machine Learning to Measure the Water Quality

Jose Fernando Alarcón Madrid, Owen Josue Paz Quintanilla, Martín Martinez-Rangel

Implementation of Boosting Algorithms in Predicting Air Quality Index of South Indian Cities

Deepasikha Mishra, Ansuman Sahu, Sreajan Naman

An Improved Data Integrity Verification Model in Cloud Computing

Nomsa L. Khumalo, Topside E. Mathonsi, Sunday O. Ojo

PRISMA Approach for Assessing Fingerprint Classification Models Based on Artificial Super-Intelligence Techniques

Alrasheed Mustafa, Richard Millham, Hongji Yang

Personalized Adaptive Learning Management System Based on Bioinspired Algorithm: A Systematic Review

Feziwe Lindiwe Yvonne Khomo, Richard Millham, Alrasheed Mustafa

Keywords Index

Authors Index

The original version of this eBook is available on https://www.scientific.net/book/engineering-innovations-vol-14/978-3-0364-1777-6

Engineering Innovations, ISSN: 2813-1002, Vol. 14, pp 1-13

doi: 10.4028/p-V2Z9mr

© 2025 The Author(s). Published by Trans Tech Publications Ltd, Switzerland.

Submitted:
2025-01-11

Revised:
2025-03-11

Accepted:
2025-03-12

Online:
2025-04-04

Tunable Hydrophobicity and Photoluminescence in Polystyrene-Calcium Carbonate Composites with Citric Acid-Induced Porosity

The original version of this paper is available on https://www.scientific.net/EI.14.1.pdf

Pan African University Life and Earth Sciences Institute (Including Health and Agriculture), PAULESI, University of Ibadan, Ibadan, Nigeria

Department of Agriculture and Environmental Engineering, University of Ibadan, Ibadan, Nigeria

Department of Wood Products Engineering, University of Ibadan, Ibadan, Nigeria

Faculty of Chemical Science, Olusegun Agagu University of Science and Technology, Ondo, Nigeria

Facultad de Ciencias Químicas, Universidad Autónoma de Coahuila, Saltillo, Coahuila, 25280, México

ademolaomooroye@gmail.com, abelolorunnisola@yahoo.com,

Keywords: photoluminescence, porous composites, calcium carbonate, oleic acid, citric acid, surface modification, adsorption, polystyrene.

Abstract

Polystyrene (PS) is widely used in industries like packaging and insulation, with calcium carbonate often incorporated as a filler to enhance mechanical properties. However, its potential in tuning surface and optical properties remains unexplored. This study addresses this gap by investigating the effects of dual modification, oleic acid for surface hydrophobicity and citric acid for controlled porosity, on PS-calcium carbonate composites. Calcium carbonate was surface-modified with oleic acid, and PS-calcium carbonate composites (40:60 weight ratio) were synthesized using the melt blending method, followed by citric acid treatment of the composites that resulted in up to 8.90% weight loss in NPS3. X-ray diffraction (XRD) and Fourier transform infrared transform FTIR analyses confirmed no chemical interaction between phases but revealed partial dissolution of calcium carbonate due to citric acid treatment. Contact angle measurements showed increased hydrophilicity after citric acid treatment, while oleic acid imparted hydrophobicity, with NPS3 and TPS3 exhibiting higher contact angles than NPS2 and TPS2. SEM images revealed matrix cavities (up to wide), especially in TPS3. Hardness testing showed a decrease in hardness with increasing oleic acid concentration, with TPS3 exhibiting the lowest value ( 60.7 Shore D). Photoluminescence measurements revealed broad emission spectra with a blue shift at lower oleic acid concentrations, while higher concentrations caused a red shift and broader emission, stabilized by citric acid treatment. Solvent absorption tests indicated enhanced absorption, with TPS3 absorbing vegetable oil and of both hexane and diesel respectively. Overall, these modifications significantly tuned the composites' mechanical, morphological, and optical properties, demonstrating potential for sensing applications.

Ayodunmomi Esther Olowofoyeku, Ademola Kabiru Aremu, Abel Olajide Olorunnisola, Ayobamiji Emmanuel Olowofoyeku, Jesus Roberto Villegas Mendez, Daniel Gbenga Adekanmi

The original version of this paper is available on https://www.scientific.net/EI.14.1.pdf

Introduction

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The development of advanced materials to meet the growing demands of modern industries has driven significant innovation in polymer composite design. Among polymers, polystyrene (PS) is widely utilized due to its affordability, versatility, and ease of processing, with applications spanning packaging, insulation, and engineering components. However, its inherent brittleness, limited impact resistance, and environmental persistence restrict its utility in high-performance and sustainable systems [1]. Addressing these challenges requires novel approaches that integrate innovative filler systems to enhance mechanical, thermal, and functional properties while improving environmental sustainability [2-4].

Fillers play a pivotal role in defining the properties of polymer composites. Calcium carbonate, an abundant and cost-effective mineral, is a popular material for reinforcing polymer matrices. Its benefits include enhanced stiffness, thermal stability, and reduced shrinkage during polymer processing, all achieved while adhering to green chemistry principles by minimizing reliance on energy-intensive or environmentally harmful additives [5, 6]. PS-calcium carbonate composites offer a versatile platform for addressing industry-specific requirements. Research demonstrates that calcium carbonate improves tensile strength, flexural properties, and thermal stability in various polymer systems, including PS [7, 8]. However, incorporating untreated calcium carbonate into hydrophobic polymers like PS presents compatibility issues, such as poor dispersion and weak interfacial bonding, which hinder composite performance [9-11].

Surface modification of calcium carbonate is used to address polymer-filler compatibility challenges. The use of fatty acids like stearic and oleic acid as surface modifiers effectively imparts hydrophobicity to the calcium carbonate surface, promoting uniform dispersion within the polymer matrix and stronger interfacial adhesion [9, 12]. Many studies have shown the effectiveness of oleic acid as a surface modifier for enhancing hydrophobicity. For example, Dweiri et al. used it to modify calcium carbonate [13], while Wang et al. applied it to carbon nanotubes [14]. while Wang et al. applied it to carbon nanotubes [15], and Kawamura et al. used it for magnetic nanomaterials [16]. Surface modification of fillers also influences the mechanical toughness, elasticity, and processability of composites. It minimizes filler aggregation, promotes uniform stress distribution, and improves the structural integrity of the material [12]. While Fatty acids are commonly employed as surface modifiers to enhance compatibility in composites, strong acids are used to treat polymers, introducing hydrophilic functional groups that significantly increase their hydrophilicity [17].

Polymers are typically not inherently photoluminescent, but incorporating materials like carbon dots or rare-earth luminescent particles transforms them into versatile composites with enhanced optical properties. This integration expands their functionality, enabling applications in advanced optical systems, solid-state lighting, display technologies, and sensors for detecting physical and biochemical parameters through luminescent responses. However, many conventional luminescent agents are expensive, require complex processing, or pose environmental risks due to rare-earth metal accumulation [18-21].

This study explores how oleic acid modification and subsequent citric acid treatment influence the tunability of structural, morphological, and photoluminescent properties in PS-calcium carbonate composites. The composites, synthesized via melt blending, were characterized using XRD for structure, SEM for morphology, and Shore hardness testing. The research further investigated the synergistic effects of oleic acid-modified calcium carbonate and citric acid-induced porosity on the composites' hydrophobicity, optical properties, and solvent adsorption for a better understanding of their potential for sustainable, high-performance applications.

Materials and Methods

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Materials

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The materials used in this study include industrial-grade calcium carbonate (particle size: , purity), pure polystyrene (PS) pellets (melt flow rate: purity), oleic acid ( purity), citric acid ( purity), ethanol ( ), and distilled water. All chemicals were sourced from reputable suppliers and used without additional purification.

Surface Modification of Calcium Carbonate with Oleic Acid

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Calcium carbonate was modified by oleic acid at varying concentrations ( , and by weight). Oleic acid was dissolved in 100 mL of ethanol, and 100 g of calcium carbonate was added to this solution. The mixture was stirred continuously for 3 h at room temperature to ensure efficient adsorption of the oleic acid onto the calcium carbonate surface. Thereafter, the ethanol was evaporated at while stirring for an additional 3 h to prevent particle agglomeration. The modified carbonates were ground and labeled based on the oleic acid concentration: C1 ( oleic
acid), C2 ( 0.75 % oleic acid), and C3 ( 2.50 % oleic acid), and then they were stored for subsequent composite preparation [22]. Wettability was assessed using a sessile drop contact angle test on a Drop Shape Analyzer (DSA30, Krüss). The contact angle was measured to quantify surface hydrophobicity, with higher angles indicating increased water repellency.

Preparation of PS-Calcium Carbonate Composites

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The PS-calcium carbonate composites were fabricated by melt blending modified calcium carbonate with pure PS using a twin-screw extruder. Processing was conducted at with a screw speed of 120 rpm for 10 minutes. The resulting extrudates were pressed into films and cut into standard test pieces. The composites were designated as NPS1, NPS2, and NPS3, based on the oleic acid-modified Calcium carbonate content. A control sample, comprising pure PS without Calcium carbonate, was processed under identical conditions and labeled as NPS [23]. The wettability of the samples was also assessed using a sessile drop contact angle test.

Citric Acid Treatment of the Materials

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Each sample (NPS, NPS1, NPS2, and NPS3) was treated with 50 mL of 1 M citric acid solution for 48 h , followed by thorough rinsing with distilled water to remove residual acid. The samples were then dried at for 12 h . The citric acid-treated composites were designated as TPS1, TPS2, and TPS3, while the treated pure PS was labeled as TPS. The weight loss due to citric acid treatment was calculated using Equation 1:

where and represent the initial and final weights of the samples, respectively.

Structure of the Composites

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X-ray diffraction (XRD) analysis was performed to examine the crystallographic structures of calcium carbonate and its composites. XRD patterns were recorded using a Malvern Panalytical Empyrean X-ray diffractometer with radiation ( ) over a range of to .

Morphology of the Composites

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The morphology of the samples was analyzed using scanning electron microscopy (SEM) with a JEOL JSM 6610LV at 600x magnification. The attached Energy Dispersive X-ray Spectroscopy (EDS) was employed to assess the elemental composition, while surface roughness profiles were analyzed using Gwyddion software.

Functional Groups

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Fourier transform infrared (FTIR) spectroscopy was used to examine the chemical interactions between PS, calcium carbonate, and the surface-modifying agents. Spectra were recorded using a PerkinElmer Spectrum Two FTIR spectrometer in the range of with the Attenuated Total Reflectance (ATR) method, providing insights into the functional groups and chemical bonding within the composites.

Photoluminescence Spectroscopy

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The photoluminescence (PL) properties of the composites were measured using a Shimadzu RF-6000 spectrofluorophotometer, with excitation at wavelengths of 255 nm and 405 nm . Furthermore, NPS3 and TPS3 samples were subjected to 10 cycles of UV irradiation at 255 nm for 5 minutes, followed by 1 h of darkness, as described by Abd El-Lateef et al. [24]. The emission peak intensity following each cycle was quantified as a percentage of their initial intensity to assess the stability of photoluminescence.

Shore Hardness

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The hardness of the PS-calcium carbonate composites was evaluated using a Shore D durometer following ASTM D2240 standards. Samples ( ) were tested at three random locations, and the average hardness value was recorded to assess the mechanical strength of the composites.

Solvent Absorption Capacity

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The solvent absorption capacity of the composites was evaluated by immersing samples in various solvents (water, vegetable oil, n -hexane, and diesel) for 24 h . After immersion, the samples were removed, gently cleaned, and weighed to determine the change in mass. The absorption capacity was calculated using Equation 2:

where and represent the initial and final weights of the samples, respectively.

Statistical Analysis

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Experiments were carried out in triplicate, and the results are reported as mean values with their corresponding standard deviations. Statistical significance was evaluated using analysis of variance (ANOVA), complemented by Dunnett's multiple comparison test ( ). All statistical analyses were performed using GraphPad Prism 8 software.

Results and Discussion

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Effect of Oleic Acid Treatment on the Wettability of Carbonate Fillers

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Fatty acid treatments, like oleic acid modification, are used to increase the hydrophobicity of fillers like calcium carbonate. This improves their dispersion in polymer matrices and reduces moisture absorption by altering the surface energy [25-27]. Fig. 1 shows the contact angle results of calcium carbonate treated with various oleic acid concentrations. While moderate oleic acid improved dispersion, excessive concentrations, as seen in C3 with a contact angle of , cause agglomeration. The stronger hydrophobic interactions between particles led to clumping, which disrupted uniformity and reduced performance in the polymer matrix [28].

Fig. 1. Contact angles of the oleic acid-treated calcium carbonate.

Fig. 1. Contact angles of the oleic acid-treated calcium carbonate.

Citric Acid Treatment and Wettability of the Composites

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As shown in Table 1, negligible weight loss was observed in pure PS (NPS), indicating minimal structural impact. In contrast, composites (NPS1, NPS2, and NPS3) exhibited significantly higher weight losses ( ), attributed to the increased porosity induced by citric acid treatment. These changes align with surface morphology modifications, highlighting the composites' responsiveness to treatments and adsorption processes. Contact angle measurements (Fig. 2) further confirm the effect of citric acid treatment, with citric acid treated composites showing reduced contact angles and increased hydrophilicity. NPS3 and TPS3 exhibited higher contact angles than NPS2 and TPS2, respectively, emphasizing the hydrophobic nature imparted by the oleic acid compared to NPS1 and TPS1. While oleic acid introduces hydrophobicity, citric acid treatment remains the dominant factor in enhancing surface hydrophilicity.

Table 1. Filler composition and sample weight loss after citric acid treatment.

Sample | Filler | Filler Oleic Acid treatment (wt%) | Filler-to-Polystyrene ratio | Weight Loss after citric acid treatment (%) | Equivalent citric acid-treated sample
NPS | -- | -- | 0:100 | 0.01 ± 0.01 | TPS
NPS1 | C1 | 0.00 | 60:40 | 9.81 ± 0.34 | TPS1
NPS2 | C2 | 0.75 | 60:40 | 9.75 ± 0.20 | TPS2
NPS3 | C3 | 2.50 | 60:40 | 8.90 ± 0.25 | TPS3

Fig. 2. Contact angles of the PS-calcium carbonate composites.

Structural Analysis

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The crystalline and amorphous characteristics of calcium carbonate fillers, PS, and their composites, as analyzed through x-ray diffraction, are shown in Fig. 3a and 3b. The diffraction patterns of modified calcium carbonate (Fig. 3a) matched ICDD card 01-070-0095, confirming a monoclinic crystalline structure with prominent peaks at , , and as has been reported in other studies [22]. In C2 and C3, oleic acid treatment reduced the intensity of these peaks due to lattice disruption and the formation of a hydrophobic coating, as also reported by similar studies [22, 29]. PS, including both untreated (NPS) and citric acid-treated (TPS), displayed its characteristic amorphous hump around , confirming its non-crystalline nature [30, 31].

The XRD patterns of PS-calcium carbonate composites (Fig. 3b) combined crystalline peaks from calcium carbonate with the amorphous hump of PS showing no chemical bonding evidence in the composites (NPS1, NPS2, NPS3), carbonate peaks dominated due to the higher intensity of the crystalline phase, overshadowing the amorphous PS peak. In citric acid-treated composites (TPS1, TPS2, TPS3), the calcium carbonate diffraction intensity decreased, attributed to its partial dissolution. The more pronounced PS hump in the citric acid-treated composites indicates that the polymer's structure remained intact after the treatment. Additionally, TPS3 exhibited stronger calcium carbonate peaks than TPS1, due to a higher surface coating of oleic acid in C3.

Fig. 3. XRD spectra of (a) oleic acid-treated calcium carbonate and (b) PS-calcium carbonate composites.

Fig. 3. XRD spectra of (a) oleic acid-treated calcium carbonate and (b) PS-calcium carbonate composites.

Functional Groups

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The Fourier transform infrared (FTIR) spectra presented in Fig. 4a and 4b confirmed the functional groups present in calcium carbonate, PS, and their composites. Calcium carbonate (C1) exhibited characteristic peaks at 1395, 1080, and , corresponding to the carbonate anion's asymmetric stretching, symmetric stretching, and out-of-plane bending vibrations, respectively [22, 28, 32]. Surface modification with oleic acid in C2 and C3 intensified the peak and introduced new peaks around , representing alkyl chains from oleic acid [22]. Pure PS (NPS) displayed aromatic absorption bands, including C-H stretching at , aliphatic C-H stretch around , aromatic ring stretching at , and out-of-plane bending near [33]. These peaks persisted in treated PS (TPS), with a slight increase in intensity in the region, suggesting enhanced surface activity due to acid treatment [17]. In untreated composites (NPS1, NPS2, NPS3), the absorption bands of PS and calcium carbonate overlapped. The carbonate peak at merged with the aromatic ring stretch, while the peak remained prominent, indicating the coexistence of the polymer matrix and filler. These overlaps, along with the absence of new peaks, confirm interfacial interactions rather than chemical bonding [34]. Citric acidtreated composites (TPS1, TPS2, TPS3) showed reduced intensities in carbonate peaks at 1395 and , reflecting the partial dissolution of calcium carbonate.

Fig. 4. FTIR spectra of (a) oleic acid-treated calcium carbonate, and (b) PS-calcium carbonate composites.

Fig. 4. FTIR spectra of (a) oleic acid-treated calcium carbonate, and (b) PS-calcium carbonate composites.

Surface Morphology and Roughness Characteristics

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The SEM micrographs in Fig. 5a show the morphological changes before and after citric acid treatment. Pure PS (NPS) has a smooth surface, which remains largely unchanged after treatment (TPS), with only minor indentations likely due to surface artifacts similar to other studies [24]. In the composites, the addition of calcium carbonate introduced particles of varying sizes, up to , dispersed throughout the polymer matrix. However, composites made with oleic acid-treated calcium carbonate (C3) in TPS3 exhibited large agglomerates, unlike TPS1, which used untreated calcium carbonate (C1). The large agglomerates of oleic acid-treated calcium carbonate filler created visible voids in TPS3, reducing its hardness compared to TPS1. Acid-treated composites (TPS1 and TPS3) displayed noticeable porosity, with irregular cavities resulting from the partial dissolution of calcium carbonate by citric acid. TPS3 showed larger dents, corresponding to the agglomerates, while TPS1 had smaller, more uniform cavities. These SEM observations confirm that the oleic acid-treated filler and increased porosity in TPS3 contributed to the material's weaker structure but improved liquid adsorption.

The surface roughness of the materials was analyzed using 3D images from the SEM micrographs (Fig. 5b), and the roughness parameters are shown in Table 2. Oleic acid and citric acid treatments influenced these properties. Untreated PS (NPS1) had a smooth surface with low roughness (RMS: ), indicating a uniform surface. In untreated composites (NPS1 and NPS3), roughness increased due to filler content and subsequent particle agglomeration in NPS3,
which showed the highest RMS ( 294.5760 nm ) and . This agrees with the observation by Hayeemasae et al. that an increased calcium carbonate content enhances a composite's tensile strength [35] (Hayeemasae & Ismail, 2021). As the oleic acid concentration increased, agglomeration intensified, creating a rough surface with noticeable peaks. Citric acid treatment caused a slight increase in roughness in pure PS, likely due to surface artifacts. In contrast, citric acidtreated composites (TPS1 and TPS3) had lower surface roughness than untreated ones, as citric acid dissolved some calcium carbonate, creating a porous structure and reducing roughness, promoting wettability and solvent adsorption.

Fig. 5. SEM images showing the surface morphology of the polystyrene composites (a) and surface roughness profiles comparing polystyrene and polystyrene composites (b).

Fig. 5. SEM images showing the surface morphology of the polystyrene composites (a) and surface roughness profiles comparing polystyrene and polystyrene composites (b).

Table 2. Roughness parameters of the polystyrene and polystyrene composites.

Sample
RMS Roughness
(Sq)
Mean Roughness
(Sa)
Maximum Peak Height
(Sp)
Maximum Pit Depth
(Sv)

NPS
95.1673 nm
71.5328 nm
0.5145 μm
0.4815 μm

TPS
98.8757 nm
71.9033 nm
0.5654 μm
0.3796 μm

NPS
151.8080 nm
109.1970 nm
0.5238 μm
0.4291 μm

TPS1
132.5170 nm
93.6770 nm
0.4272 μm
0.5727 μm

NPS3
294.5760 nm
255.2710 nm
0.6630 μm
0.3369 μm

TPS3
197.4100 nm
150.8040 nm
0.5469 μm
0.4530 μm

Photoluminescence

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The emission spectra (Fig. 6a(i) and 6b(i)) and CIE chromaticity diagrams (Fig. 6a(ii) and 6b(ii)), recorded at 255 nm and 405 nm excitation wavelengths, reveal the effects of oleic acid and citric acid treatments on the fillers and composites. The broad emission peaks observed are consistent with previous studies on polymeric materials [36-38]. Interestingly, as the excitation wavelength was increased, the emission peaks exhibited both a shift and a narrowing, indicating a wavelengthdependent luminescent response [38]. At 255 nm excitation, all samples showed broad emission peaks centered around 406 nm , with TPS composites displaying the broadest curves, even wider than NPS composites. Citric acid-treated composites (TPS1, TPS2, TPS3) exhibited narrower emission peaks
and a shift to lower wavelengths compared to their counterparts (NPS1, NPS2, NPS3), indicating that oleic acid treatment reduced defect-related emissions. In contrast, citric acid treatment introduces defect states, broadening the emission and causing a red shift. At 405 nm excitation, the emission peaks were centered around 570 nm , with pure NPS showing the lowest intensity, slightly below pure TPS. NPS3 displayed the highest intensity, while the citric acid-treated TPS composites exhibited intermediate intensities and a slight shift toward longer wavelengths compared to the NPS composites. These findings suggest that oleic acid treatment enhances light emission intensity and shifts the color to shorter wavelengths. In contrast, citric acid treatment introduces defects that reduce intensity and shift the color to longer wavelengths. CIE color maps (Fig. 6a(ii) and 6b(ii)) show a cluster in the blue-violet region under 255 nm excitation, shifting towards yellow-green under 405 nm excitation. Additionally, NPS3 and TPS3 demonstrated remarkable photoluminescent stability after 10 cycles of excitation and relaxation at 255 nm (Figures 7a and 7b), highlighting the tunability and stability of the composites' photoluminescent properties. Oleic acid treatment improves luminescence at lower concentrations, while citric acid treatment stabilizes radiative transitions and enhances photoluminescence, offering a tunable approach to modifying the optical properties of these materials.

Fig. 6. Photoluminescence emission spectra at excitation wavelengths of 255 nm (a(i)) and 405 nm (b(i)), along with CIE color map spectra for 255 nm (a(ii)) and 405 nm (b(ii)).

Fig. 6. Photoluminescence emission spectra at excitation wavelengths of 255 nm (a(i)) and 405 nm (b(i)), along with CIE color map spectra for 255 nm (a(ii)) and 405 nm (b(ii)).

Fig. 7. Photoluminescence stability of (a) NPS3 and (b) TPS3 after 10 cycles of excitation and relaxation at 255 nm .

Fig. 7. Photoluminescence stability of (a) NPS3 and (b) TPS3 after 10 cycles of excitation and relaxation at 255 nm .

Shore Hardness Results

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The Shore hardness values in Fig. 8a show changes due to the incorporation of composites, reflecting their mechanical performance. Untreated and treated PS (NPS and TPS) had a hardness of 78.30, while the addition of carbonate fillers initially increased hardness, peaking in NPS1 and TPS1, then declining with higher oleic acid treatment in NPS3 and TPS3. The decline in hardness was attributed to surface modifications that induced agglomeration and porosity. Toro et al. and

Hayeemasae et al. found that the increased particle size of eggshell fillers in PS composites led to reduced tensile strength [35]. The ANOVA results show a significant difference among the means ( ), with a strong model fit ( R -squared ). Dunnett's test revealed significant differences in hardness for all treatments, while the low residual variance ( ) indicates minimal error in the data. This decline can be attributed to the dual effects of surface modifications and porosity introduced during oleic acid and citric acid treatments. This observed softening was attributed to the formation of agglomerates after the oleic acid treatment, which reduces its bonding strength within the polymer matrix. The citric acid treatment, on the other hand, dissolved portions of the calcium carbonate, creating pores that softened the composite structure. These two factors contributed to the decline in the hardness.

Solvent Affinity Test

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The graph in Fig. 8b presents the weight gain percentage of the materials when immersed in water, vegetable oil, hexane, and diesel. Samples NPS and TPS showed minimal weight gain compared to the other composites, indicating low porosity and poor absorption capacity. In contrast, citric acidtreated composites (TPS1-TPS3) demonstrated a marked increase in absorption for the solvents, which aligns with the increased porosity, which increased the surface area available for absorption and the composite's modified surface, which increased hydrophobicity. The highest weight gain is observed in TPS3. Interestingly, water absorption remains relatively low across all samples, underscoring the hydrophobic nature of the treated composites due to the matrix. Treated composites traded some hardness for better absorption, and they have displayed a preference for nonpolar solvents.

The ANOVA statistical analysis presented in Table 3 confirms that both the solvent and sample materials significantly influence adsorption, with very low P -values ( ) and high F -values (4067.40 for solvent and 3294.97 for sample materials). The interaction between solvent and sample materials also plays a significant role ( ), accounting for of the total variance. This suggests that both factors have a major impact on absorption and interact in a way that depends on the material type. Dunnett's multiple comparisons test further highlighted significant differences in absorption for all treatments compared to water, reinforcing the robustness and statistical significance of the results . The results show that both the type of solvent and the materials being tested strongly influence how much they absorb, with both factors having a very big impact. Additionally, the way the solvent interacts with the materials changes depending on the material type, making their combined effect significant. This means that neither the solvent nor the material behaves the same across all conditions, which is important when predicting performance in real-world applications.

Fig. 8. (a) Hardness measurements of the composites, and (b) solvent absorption capacity.

Fig. 8. (a) Hardness measurements of the composites, and (b) solvent absorption capacity.

Table 3. ANOVA analysis summary for the solvent affinity test.

Source of Variation | Degrees of Freedom (DF) | Sum of Squares (SS) | Mean Square (MS) | F Value | P Value | Variance Contribution (%)
Solvent | 3 | 1623 | 541.0 | 4067.40 | < 0.0001 | 30.76
Sample Materials | 7 | 3068 | 438.3 | 3294.97 | < 0.0001 | 58.15
Interaction | 21 | 576.7 | 27.46 | 206.46 | < 0.0001 | 10.93
Residual (Error) | 64 | 8513 | 0.1330 | - | - | 0.16
Total | 95 | 5276 | - | - | - | 100.00

Conclusion

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This study investigated the impact of oleic acid and citric acid treatments on the properties of PS-calcium carbonate composites, focusing on hydrophobicity, morphology, photoluminescence, hardness, and solvent adsorption. The carbonate fillers were treated with various concentrations of oleic acid before being incorporated into the polymer matrix via melt blending. The resulting composites were subsequently treated with citric acid. Key findings indicate that oleic acid treatment enhanced hydrophobicity but reduced surface hardness, whereas citric acid treatment increased porosity through calcium carbonate dissolution. XRD and FTIR analyses confirmed the presence of all composite components, with no evidence of chemical bonding between the filler and polymer matrix. SEM images revealed that higher concentrations of oleic acid led to filler agglomeration, while citric acid treatment increased surface roughness and porosity. Photoluminescence spectra revealed that oleic acid treatment narrowed emission peaks, enhancing luminescence, whereas citric acid induced defect states, leading to emission shifts. Shore hardness measurements indicated a reduction from 83.20 to 60.70 , while solvent absorption tests demonstrated a trade-off between mechanical properties and absorption capacity, with vegetable oil exhibiting the highest absorption . Overall, oleic acid treatment improved hydrophobicity and photoluminescence, while citric acid treatment primarily contributed to porosity and hydrophilicity. These findings demonstrate that oleic acid and citric acid treatments provide a tunable strategy for modifying PS-calcium carbonate composites, enhancing their potential for adsorption and smart pollutant detection applications.

Acknowledgments

The original version of this paper is available on https://www.scientific.net/EI.14.1.pdf

Ayodunmomi Esther Olowofoyeku sincerely thanks the African Union and the Pan African University Life and Earth Sciences Institute for awarding a Ph.D. scholarship for this research as a part of her doctoral research.

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The original version of this eBook is available on https://www.scientific.net/book/engineering-innovations-vol-14/978-3-0364-1777-6

Engineering Innovations, ISSN: 2813-1002, Vol. 14, pp 15-23

doi: 10.4028/p-kVcA1n

© 2025 The Author(s). Published by Trans Tech Publications Ltd, Switzerland.

Submitted:
2024-12-27

Revised:
2025-03-03

Accepted:
2025-03-05

Online:
2025-04-04

Study of Kevlar Aramid Fiber in Improving Concrete Performance and Mitigating Pathologies

The original version of this paper is available on https://www.scientific.net/EI.14.15.pdf

Amanda de Queiroz Mendes , Danielle Ferreira dos Santos

¹Civil Engineering Department, Centro Universitário Serra dos Órgãos, UNIFESO, 25976-345, Terezopolis, RJ, Brazil

amandaqueirozm21@hotmail.com, daniellesantos@unifeso.edu.br

Keywords: Kevlar reinforcement, Concrete durability, Structural pathology mitigation, Mechanical performance enhancement.

Abstract

This study investigated the potential of Kevlar aramid fiber reinforcement to enhance the mechanical properties and mitigate pathologies in concrete structures. The objectives were to analyze improvements in flexural and compressive strengths and to evaluate the effectiveness of Kevlar in reducing pathological manifestations under the specific conditions and methodologies of this experiment. Experimental testing was conducted on prismatic and cylindrical concrete specimens, prepared following standard mix ratios and testing protocols. Kevlar fiber was applied to the specimens using an epoxy resin, ensuring adhesion and uniform reinforcement. The results, obtained under these controlled conditions, showed a increase in flexural tensile strength, from 0.29 MPa to 0.31 MPa , and a enhancement in compressive strength, from 14 MPa to 16 MPa . Pathological manifestations, such as crack propagation, were significantly mitigated in the reinforced specimens, demonstrating improved structural preservation and reduced degradation under tensile and compressive stresses. These findings, specific to the experimental parameters and standards employed, confirm the dual benefits of Kevlar reinforcement. The study highlights its effectiveness in improving durability, reducing maintenance demands, and extending the service life of concrete structures, reinforcing its potential as a viable solution for advanced civil construction applications.

Introduction

The original version of this paper is available on https://www.scientific.net/EI.14.15.pdf

The demand for innovative materials in civil construction has driven the adoption of highperformance reinforcements, such as Kevlar aramid fiber, to enhance the durability and structural performance of concrete elements. Numerous studies have demonstrated the effectiveness of fiber reinforcements in improving concrete's mechanical properties and mitigating structural deficiencies. Kevlar, known for its exceptional mechanical properties such as high tensile strength, lightweight composition, and chemical resistance, has been increasingly applied in construction to address common structural issues [1,4]. These attributes make it an ideal reinforcement material for mitigating structural pathologies and improving the longevity of construction projects [5].

Chemically, Kevlar's molecular structure is characterized by aromatic polyamide chains, which provide its exceptional strength-to-weight ratio and thermal stability. The hydrogen bonds within its structure contribute to its remarkable tensile properties, while its resistance to environmental degradation ensures long-term performance in harsh conditions . These characteristics are particularly advantageous in civil construction, where structures are frequently exposed to cyclic loads, environmental stressors, and other factors that induce damage over time [9,10].

Chemically, Kevlar's molecular structure consists of aromatic polyamide chains, which provide its strength-to-weight ratio and thermal stability. These properties contribute to its resistance to environmental degradation, making it an ideal reinforcement material in civil construction, where structures are exposed to cyclic loads and environmental stressors [6,8]. Research has shown that Kevlar-reinforced concrete exhibits significant improvements in flexural tensile and axial compression strengths, as well as a reduction in pathological manifestations such as cracks and delamination [11,15].

Beyond improving mechanical properties, the use of Kevlar also contributes to reducing common structural pathologies, such as cracks widening and material delamination [16,18]. Pathological manifestations are a primary concern in civil construction, as they can compromise the safety and functionality of structures. Kevlar's reinforcement capabilities enhance crack control, thereby mitigating damage and reducing the frequency and cost of maintenance. Despite its advantages, a gap persisted in the application of Kevlar to simultaneously enhance mechanical performance and mitigate pathologies in civil construction. This study addressed this gap by systematically evaluating the effects of Kevlar reinforcement on the mechanical properties and pathology mitigation of concrete structures. The objectives of the research were to analyze the improvements in flexural and compressive strengths and to evaluate the effectiveness of Kevlar reinforcement in reducing pathological manifestations in concrete [19,20].

The results revealed a increase in flexural tensile strength, rising from 0.29 MPa to 0.31 MPa , and a improvement in compressive strength, from 14 MPa to 16 MPa . Pathological phenomena, such as crack propagation, were significantly reduced in the reinforced specimens, highlighting superior structural integrity and reduced degradation under tensile and compressive stresses. This study highlights the dual benefits of Kevlar in civil construction: its ability to enhance mechanical performance and its role in minimizing structural pathologies [21,24]. These findings underscore the material's potential as a sustainable and efficient solution for improving the reliability, safety, and durability of concrete structures, particularly in applications requiring high-performance reinforcements.

Experimental

The original version of this paper is available on https://www.scientific.net/EI.14.15.pdf

Materials. The materials used in the study were selected based on their specific technical properties, as described below. The binder employed was Portland cement CPII-E-32, supplied by the Mauá brand, whose composition meets Brazilian technical standards for the production of intermediate-grade concrete, characterized by a compressive strength of 32 MPa at 28 days, and having durability properties suitable for the proposed application. The coarse aggregate used was crushed stone 02, purchased from the Arckom brand, with a granulometry ranging from 4.8 to 9.5 mm , meeting the specifications of NBR 7211 [25] for coarse aggregates for concrete. This aggregate is classified as high-strength, with a compressive strength index greater than 40 MPa , ensuring the robustness and structural integrity of the concrete. The fine aggregate used was medium sand, also supplied by the Arckom brand, with a granulometry ranging from 0.3 to 0.6 mm , according to NBR 7211 [25]. The sand was selected for its good workability and adhesion characteristics, as well as its satisfactory abrasion resistance for the concrete mix. The water used for preparing the mixture came from the public water supply, meeting the quality criteria established by NBR 7222, ensuring the absence of impurities or harmful substances to the concrete. The release agent used for the test specimens was Desmol Cd 3.6 liters, supplied by Otto Baumgart, chosen for its high efficiency in demolding the concrete without compromising the surface integrity of the specimens. This product is characterized by its release properties suitable for high-strength concretes, not interfering with the curing process or the final properties of the concrete. The Kevlar aramid fibers used in the study were purchased from Tapcamp, with a tensile strength of 3.62 MPa and an elastic modulus of 120 GPa . The fibers had a length of 50 mm and a diameter of 12 micrometers. Kevlar was chosen for its exceptional mechanical strength and toughness properties, which contribute to improving the strength and durability characteristics of the concrete. The Kevlar fibers were added to the concrete to increase tensile strength and crack resistance, as well as to provide greater ductility to the material. The epoxy resin used was 4008, of low viscosity and ultra-clear, supplied by Redelease. This resin has ideal characteristics for fiber impregnation, with a transparency index greater than and UV protection, ensuring the durability of the material when exposed to sunlight. The resin was accompanied by the hardener ( 715 g ), which, when mixed with the resin, provided proper curing and the formation of a robust and durable compound.

The water and resin were carefully dosed to ensure the workability of the concrete and the effective adhesion of the fibers to the concrete matrix. The properties of each material were thoroughly analyzed to optimize the performance of the concrete reinforced with Kevlar fibers. Figure 1 illustrates the materials used in the preparation of the concrete, for later coating with Kevlar fiber, highlighting the composition aspects of the concrete and the fiber impregnation process.

Fig. 1. (A) materials used in preparing the concrete mix (crushed stone (a), sand (b), and cement (c)); (B) mixing materials in an electric concrete mixer.

Fig. 1. (A) materials used in preparing the concrete mix (crushed stone (a), sand (b), and cement (c)); (B) mixing materials in an electric concrete mixer.

Avaliation of Workability and Moldability of Samples. In the study conducted, a classic material proportion was used for concrete production, following the ratio, corresponding to 1 part cement, 2 parts sand, and 3 parts gravel. This mix is widely applied in simple concrete applications, such as sidewalks and subfloors, as it offers a good balance between workability, strength, and cost. The water amount was adjusted based on a water-to-cement ratio of 0.5 , ensuring proper consistency without compromising the material's strength. This proportion provides homogeneous concrete with good workability and durability for non-structural applications. The slump test was performed to evaluate the consistency and ensure the quality of the concrete. The test was conducted in accordance with the NBR 16889 standard [27], and the measured slump of the adopted concrete was 10 cm , as illustrated in Figure 2.A. Prior to the concrete placement, a coat of form release agent was applied to each mold to facilitate the demolding of the specimens. Subsequently, the CA-60 steel reinforcement was positioned within the prismatic molds using circular and chair spacers to ensure a nominal cover of 3 cm , as specified in the NBR 6118:2023 standard [28]. The flexural design calculation of the prismatic samples was based on the NBR 6118 standard [28] to ensure structural integrity. For this purpose, the minimum reinforcement calculation was employed, defined as the one where steel bars were placed in the tensile region, while the compressive region relied exclusively on the concrete to balance the compression forces. In the tensile zone, reinforcement with a spacing of 60 cm was used.

The concrete compaction was performed using an immersion vibrator with a 2.5 cm diameter hose for the prismatic samples and manually, using a metallic rod, for the cylindrical samples. The latter received 25 uniformly distributed strikes over the section of each concrete layer.

The samples were demolded and stored in a curing tank 24 hours after molding (Figure 2). It was observed that the concrete exhibited good appearance, with a uniform texture and no defects such as honeycombing, as illustrated in Figure 3.

Fig. 2. (A) Slump test; (B) preparation of beam molds; (C) Specimens obtained from concrete beams.

Fig. 2. (A) Slump test; (B) preparation of beam molds; (C) Specimens obtained from concrete beams.

These samples (Figure 3) remained submerged in water, maintained at a temperature of , in accordance with NBR 9479 [29]. After 28 days in the curing tank, the samples were removed for the subsequent research phase: the application of fiber to the matrix and the execution of strength tests.

Fig. 3. Specimens immersed in a tank for wet curing. (A) flexural stren gth specimens; (B) compressive strength specimens.

Fig. 3. Specimens immersed in a tank for wet curing. (A) flexural stren gth specimens; (B) compressive strength specimens.

Preparation of Concrete Specimens Reinforced with Kevlar Fiber. The selection of aramid fiber was based on its high-performance properties and lightness. The fiber fabrics were cut to the specific dimensions required to cover the bottom face of the prismatic samples and the lateral face of the cylindrical samples, both bonded longitudinally. To ensure adhesion to the matrix, an epoxy resin mixed with a hardener in a 2:1 ratio (two parts resin to one part hardener) was used, strictly following the manufacturer's specifications.

After the initial application of the fiber, an additional layer of resin was applied over the fabric, initiating the material's curing process. This process required 24 hours to achieve maximum resistance and stiffness properties. Figure 4 illustrates the coating applied to the test specimens.

Fig. 4. Coating of the specimens with Kevlar fiber for flexural and compressive strength tests.

Fig. 4. Coating of the specimens with Kevlar fiber for flexural and compressive strength tests.

Mechanical Tests of Materials. The flexural tensile strength test was performed in strict accordance with the procedural requirements outlined in NBR 12142 [30], which provides the standardized methodology for conducting such tests. A total of twelve prismatic specimens, each with dimensions of , were selected, accurately numbered, and identified for testing. The specimens were segregated into two categories: reinforced concrete with fiber coating and unreinforced concrete. Markings were inscribed on the tensile surface and the opposite face of each specimen to ensure proper central alignment within the loading apparatus. Additionally, reference lines were drawn 5 cm from the edges and at the 25 cm mark along the length of the specimens, ensuring precise alignment of the loading apparatus along the span. Upon correct positioning, the samples were subjected to a progressively applied load, without any shock impact, at a rate of to , until failure was observed. The flexural tensile strength was evaluated based on the rupture occurring within the central third of the span between the supports, or alternatively, rupture occurring outside this region. Figure 5 illustrates the execution of the 3PB test using the INTERMETRIC universal mechanical testing machine, with a maximum capacity of 100 kN .

Fig. 5. Flexural strength test.

Fig. 5. Flexural strength test.

In the axial compression resistance test, twelve cylindrical samples were identified and numbered to initiate the experiment. These samples were divided into two groups: reinforced concrete and plain white concrete without fiber reinforcement. Subsequently, the samples were positioned between two compression plates that applied continuous and uniform loads without shocks. The loads were applied at a loading rate ranging from to until the specimens failed. Figure 6 illustrates the axial compression test performed on samples reinforced with Kevlar and on those without reinforcement.

Fig. 6. Compressive strength tests.

Fig. 6. Compressive strength tests.

Results and Discussion

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Tensile Strength Test in Flexion. After the rupture of the uncoated samples, it was observed that the cracks caused by overload exhibited wider openings in the regions subjected to tensile stresses. The cracks observed in Figures 7.A and 7.B were characterized as predominantly vertical and located in the tensile zone of the samples, indicating the typical behavior of materials subjected to bending stresses. These cracks developed continuously, starting from the lower surface of the samples and propagating toward the central region, demonstrating the overcoming of the concrete's tensile strength. Additionally, signs of stress concentration were identified in areas near the cracks, suggesting a pattern of failure controlled by the mechanical stresses applied during the test. The flexural tensile strength test showed that Kevlar-reinforced prismatic samples exhibited an average resistance of 0.31 MPa compared to 0.29 MPa in uncoated samples, representing a 6.90% increase. This improvement is consistent with findings from similar studies, which reported enhanced stress distribution and reduced crack propagation in fiber-reinforced concrete [11,16]. Despite the occurrence of cracks in both groups, the reinforced samples demonstrated superior structural preservation, aligning with theories of improved energy absorption in fiber-reinforced materials [17,19].

Fig. 7. Specimens after the flexural strength test. (A) Uncoated beam with fibe; (B) Beam with fiber coating.

Fig. 7. Specimens after the flexural strength test. (A) Uncoated beam with fibe; (B) Beam with fiber coating.

Aramid fibers exhibit high tensile strength, enabling them to withstand significant stress before failure, thereby contributing to the preservation of structural integrity. In essence, the fibers act as a network that restricts the propagation of cracks, thereby enhancing the material's durability and strength.

Axial Compression Resistance Test. The uncoated cylindrical samples exhibited cracks with Type E - sheared and Type D - conical and sheared patterns on the external surface, as illustrated in Figure 8. The same procedure was performed on the cylindrical samples coated with aramid fiber. Due to the presence of fiber in the specimens, greater structural preservation was observed when subjected to axial compression strength tests (Figure 8). The axial compression tests revealed a increase in strength for Kevlar-coated cylindrical samples, with average resistances of 16 MPa compared to 14 MPa in uncoated specimens. This finding corroborates past research indicating that fiber reinforcements enhance load distribution and prevent catastrophic failure under compressive stresses [12,18]. The Kevlar fibers minimized surface cracking and structural deformation, further supporting their utility in maintaining structural integrity under high loads.

Fig. 8. Specimen fractured after the axial compression resistance test. (A) Uncoated beam with fibe; (B) Beam with fiber coating.

Fig. 8. Specimen fractured after the axial compression resistance test. (A) Uncoated beam with fibe; (B) Beam with fiber coating.

Comparative Analysis. The results align with existing literature, emphasizing the reinforcing effect of aramid fibers in improving both flexural and compressive strengths. The incorporation of Kevlar also addresses common construction pathologies, including crack widening and material delamination, as reported in previous studies [20,22]. Table 1 summarizes the mechanical performance improvements achieved with Kevlar reinforcement. The analysis of the data obtained from the flexural and compressive strength tests showed that the addition of Kevlar resulted in significant improvements in the material properties. Flexural strength increased from 0.29 MPa to 0.31 MPa , representing an improvement of approximately , while compressive strength increased from 14 MPa to 16 MPa , resulting in a increase.

Table 1. Mechanical Performance Improvements.

Property (MPa) | without Kevlar | with Kevlar | Improvement (%)
Flexural tensile | 0.29 | 0.31 | 6.90
Compression resistance | 14.0 | 16.0 | 14.0

The data obtained from the flexural tensile and axial compression strength tests highlighted the significant benefits of Kevlar fiber reinforcement in improving the mechanical performance of concrete. Flexural tensile strength increased from 0.29 MPa to 0.31 MPa , representing an improvement of approximately . This increase demonstrates Kevlar's ability to distribute stresses more uniformly, reducing crack propagation in the concrete matrix, particularly in areas subjected to tensile stresses.

Similarly, axial compression strength showed a substantial increase, from 14 MPa to 16 MPa , equivalent to a improvement. This result reflects Kevlar's effectiveness in enhancing the structural integrity of concrete, reducing the formation of typical failures such as conical or sheared fractures observed in unreinforced specimens. This improvement also reinforces Kevlar's role as an effective reinforcement material in high-load contexts, where durability and structural preservation are essential.

These gains in mechanical strength not only confirm Kevlar's potential for practical applications but also emphasize its contribution to mitigating common pathologies, such as the formation and expansion of cracks. By controlling these pathological manifestations, Kevlar reinforcement not only increases structural durability but also reduces maintenance costs over the lifetime of constructions. These findings corroborate previous studies that identified similar benefits in the use of aramid fibers in concrete, aligning with theories on the effectiveness of high-strength reinforcements in advanced civil construction contexts.

Conclusions

The original version of this paper is available on https://www.scientific.net/EI.14.15.pdf

This study demonstrates that Kevlar aramid fiber significantly enhances the mechanical performance and durability of concrete structures. The research objectives were addressed by showing a increase in flexural tensile strength, from 0.29 MPa to 0.31 MPa , and a improvement in axial compression strength, from 14 MPa to 16 MPa , along with a significant reduction in pathological manifestations such as crack propagation and structural deformities in Kevlar-reinforced samples. These findings confirm the potential of Kevlar as a high-performance material for sustainable and reliable civil construction applications. Future studies could explore the long-term performance of Kevlar reinforcement under varying environmental conditions.

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The original version of this paper is available on https://www.scientific.net/EI.14.15.pdf

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The original version of this eBook is available on https://www.scientific.net/book/engineering-innovations-vol-14/978-3-0364-1777-6

Engineering Innovations, ISSN: 2813-1002, Vol. 14, pp 25-36

doi: 10.4028/p-hJ71eo

© 2025 The Author(s). Published by Trans Tech Publications Ltd, Switzerland.

Submitted:
2024-07-03

Revised:
2024-10-21

Accepted:
2024-11-05

Online:
2025-04-04

Smart Architecture: Case Studies and Breakthrough Applications of Advanced Materials in Buildings

The original version of this paper is available on https://www.scientific.net/EI.14.25.pdf

Valentina Frighi

¹Department of Architecture, University of Ferrara, via della Ghiara 36, Ferrara, Italy

valentina.frighi@unife.it

Keywords: Smart Architecture, advanced materials, building envelopes, biomimicry, smart materials, kinetic elements, 3D printing.

Abstract

Buildings have become experimental grounds for architectural technology, sustainable practices, and human-centered design principles to be tested and refined. This paradigm shift has not only transformed the physical aspects of urban environments but has also redefined the relationship between architecture, end-users, and the built environment. Advancements in technology paved the way for a revolutionary approach to architecture, that involves responsiveness and adaptability to the environment, leading to the spread of the so-called Smart Architecture, buildings able to fit in with their ever-changing surroundings. Smart buildings present, in general terms, a global enhancement of their performance features, having the potential to impact the built environment in a new interacting, and engaging way, making architecture more accessible, performant, and user-friendly. This paper summarizes the results of a study aimed at identifying and classifying a sample of applications of advanced materials and technologies in the context of building envelopes, considered representative of relevant Smart Architecture solutions. The categorization will be done according to four categories: biomimicry, smart materials, kinetic elements, and 3D printed solutions. This results in a database of Smart Architecture case studies that collects brief details of each application and working principles, together with data regarding design practice, technology readiness, and economic aspects, among others.

Introduction

The original version of this paper is available on https://www.scientific.net/EI.14.25.pdf

The history of Architecture has been profoundly influenced by technological advancements, societal needs, and environmental concerns.

In recent years, there has been a notable shift toward the adoption of most recent innovations within the built environment, leading to the development of the so called "Smart Architecture", an approach that emphasizes the integration of advanced materials and technologies to create buildings that are responsive, adaptive, and environmentally sustainable. This has not only reshaped the urban landscape but has also redefined the way architects, engineers, and designers conceptualize and engage with the built environment.

In this definition of the concept of Smart Architecture, building envelopes play a central role as outer layer of buildings that serve as primary interface with the external environment. Therefore, the integration of advanced materials and technologies within building envelope domain holds the potential to revolutionize the way buildings are designed, constructed, and operated, improving their sustainability, efficiency, and responsivity, according to the needs of their occupants.

Objective of the Study and Methodology

The original version of this paper is available on https://www.scientific.net/EI.14.25.pdf

The paper aims to give a broad overview about the diverse range of applications of advanced materials and technologies within the building envelope domain, focusing on their role in shaping the emergence of Smart Architecture.

Through a systematic categorization of case studies - here presented only via a reduced sample, for synthesis purposes - the contribute seeks to identify common themes, challenges, and opportunities associated with the integration of advanced materials and technologies in building
design. By combining insights from the sample analyzed indeed, the paper aim is to contribute to a deeper understanding of the potential implications of Smart Architecture for the built environment and society at large.

The methodology employed provides for the systematic identification and classification of case studies according to different categories, recognized as relevant "approaches" to reach smartness in Architecture through the application of building advanced materials and technologies. A comprehensive literature review was conducted to identify a number of relevant case studies from books and journals, academic sources, conference proceedings, and industry reports. The case studies were then categorized, and, for each case study, relevant information was extracted, including details of the application's working principles, design practices, technology readiness, economic aspects, and other pertinent factors. This information was then compiled into a database (Fig.1), allowing for systematic analyses and comparisons across different case studies. Additionally, qualitative analysis techniques, such as thematic analysis, were employed to identify common themes, challenges, and opportunities emerging from the case studies.

Fig. 1. Extracted from the database created for data collection related to the case- studies analyzed.

Fig. 1. Extracted from the database created for data collection related to the case- studies analyzed.

Smart Architecture definition

The original version of this paper is available on https://www.scientific.net/EI.14.25.pdf

Although the use of the term "intelligent" in the context of building regulation is relatively recent, some of the principles underlying this concept (such as the use of Building Automation and Control Systems), as well as their interaction with installations or other technical devices (Technical Building Systems), are well-known and already largely integrated, and have been addressed in last energy efficiency regulations. The concept of "smart buildings" indeed, so as the references to "smart technologies" are included in the Directive 2018/844/EU [1], published in 2018 as an amendment of the EPBD; in the context of a strong digitalization trend and the rapid development of related markets, smart building technologies, and Information and Communication Technologies (ICTs) as well, are seen as drivers for energy efficiency in buildings. The technologies on which these systems are based have also existed for some time, implemented in various services with varying levels of possible control.

However, there is no commonly accepted definition of "smart buildings" or even "smart technologies" in the regulatory context, despite various initiatives being promoted in this direction, both nationally and internationally.

In its early stages, the smart concept referred more to automation than intelligence [2]; even if, the real spread of the concept of smartness came in the century, alongside the soaring of new materials and innovative technologies experimentations [3].

In general terms, we can say that Smart Buildings present improved performance characteristics that, in most cases, aim to confer them some degree of adaptability, concerning both the surrounding environment and their end-users, contrasting the main deficiency of "traditional buildings" which stands in their static behavior [3].

Simplifying, the concept of building intelligence underlying the one of smart architecture, can be substantiated according to two different approaches: the first aimed at acting on technical devices, to optimally monitor and manage the building-plant system; and the second aimed at implementing the performance of the building envelope, through the use of innovative materials, techniques, and technologies capable of providing buildings with the aforementioned capabilities, even in a passive manner. Increasingly frequent, of course, are examples where these approaches converge towards a common goal: creating buildings that are not only intelligent, capable of adapting to the continuously changing needs of the current context, but also interactive and engaging for the users, making architecture high-performing, accessible, and inclusive.

Smart buildings, understood in the broadest sense of the term - that is to describe both intelligent and adaptive systems [4] - can therefore be considered one of the latest frontiers of technological research, linked to the desire to design increasingly high-performing envelope models which, with the help of advanced devices (such as, but not limited to, sensors or smart materials), give rise to systems capable of reducing the building's energy consumption, managing its various related variables to usage, user preferences, external conditions, and, more generally, the complexity of the building. Technological progress has indeed paved the way for an entirely new approach to architecture, which considers change as an essential part of the process, embracing the concepts of reactivity and adaptability towards the spread of so-called intelligent architecture, made up of buildings capable of dynamically adapting to a continuously evolving surrounding environment.

Categorization

The original version of this paper is available on https://www.scientific.net/EI.14.25.pdf

The paper aims to briefly depict the results of a study aimed at identifying and classifying a sample of applications of advanced materials and technologies within the building envelopes' domain, considered representative of relevant smart architecture solutions.

In an attempt to categorize the various design possibilities capable of giving buildings the required "intelligence", it seems possible to refer to different groups that encompass significant solutions or approaches that "enable" the capacities for reaction and adaptation to change, helping building envelope to be more responsive towards both external and internal conditions. Therefore, the "categories" identified are: biomimicry, smart materials, kinetic elements, and 3D printed solutions.

The first category is biomimicry (from the Greek, βίος, life, and μίμησις, imitation), a discipline that aims to transfer the principles that regulate and define the functioning of natural organisms to the field of architecture. Specifically, the term biomimetics refers to building envelope solutions inspired by nature [5], where it is possible to find a vast database of adaptive, multifunctional, and reactive strategies, whose observation is a great source of inspiration for the conception of adaptive technological systems [6].

Fig. 2. Details of the stairwell of the Gaudi's Sagrada Familia in Barcelona, doubling a seashell, example of how natural forms can be used as design inspiration. Source: Brendan Baker via Flickr under the CC BY-NC-ND 2.0 license.

Fig. 2. Details of the stairwell of the Gaudi's Sagrada Familia in Barcelona, doubling a seashell, example of how natural forms can be used as design inspiration. Source: Brendan Baker via Flickr under the CC BY-NC-ND 2.0 license.

Biomimetic solutions can develop, through different mechanisms of adaptation, functional surfaces, dynamic movements, or material development based on biomimetic design [7], or, again, new building envelope systems able to provide multifunctional highly advanced responsive technology in a new adaptive form [8]. Sometimes, biomimetic adaptive envelopes are characterized by low-tech technological solutions, capable of passively adapting to external conditions and thus limiting the energy consumption of the buildings that house them [5].

However biomimetics is a rapidly growing discipline in engineering and architectural design [9], it's scarcely applied in architecture; the majority of applications are still limited to prototypes or small-scale pavilions [5] since transforming natural strategies into technologies for the adaptivity of building envelope is still a great challenge.

Another possibility to lend building smartness is given by using materials with unprecedented characteristics, such as the so-called smart materials, able to react and/or change their properties according to the stimuli they receive [10]. In response to various changes, whether or not of environmental nature (temperature, humidity, or light, to name a few), smart materials enable the creation of dynamic and interactive building envelopes, based on different control strategies.

Fig. 3. Aerogel is one of the best examples of smart material thanks to its extreme lightness and strength. Source: Courtesy NASA/JPL-Caltech, Public domain, via Wikimedia Commons

Fig. 3. Aerogel is one of the best examples of smart material thanks to its extreme lightness and strength. Source: Courtesy NASA/JPL-Caltech, Public domain, via Wikimedia Commons

Within literature, smart materials are described as intelligent or active materials, or more in general, as a group of material systems with unique properties [11]. Among them are: piezoelectric materials, which release an electric charge when they undergo deformation or, conversely, deform when subjected to an electric voltage; shape-memory materials, such as alloys or polymers that deform due to temperature changes but that can return to their original shape; magnetostrictive materials, which can change shape when subjected to a magnetic field or generate a magnetic field when subjected to mechanical deformation; chromogenic materials, which change their optical and
transparency characteristics in response to thermal, optical, or electrical changes; photomechanical materials, self-healing materials, or dielectric elastomers. The application of these materials allows to exploit their unique capabilities to endow architecture with the much-desired ability to adapt.

Among the wide scope of possibilities related to their adoption, of particular interest is their application to transparent building envelope systems, due to the potential they offer in terms of controlling and managing incident solar radiation, by changing transparency, color, or shapes. For instance, photochromic materials such as smart glass, able to adjust their color and transparency, can respond to light changes to regulate the amount of light entering a building, allowing for privacy within interior spaces, and light control for exterior windows.

Thanks to their sensing abilities, smart materials can be considered the best allies of advanced architecture, enabling building envelope with a responsive function, able to regulate temperature, solar radiation, and, in general, properly behave in different climatic conditions, properly reacting to variable environmental parameters. The use of smart materials indeed, favors the adaptability of the building envelope to environmental conditions and improves not only the resilience of buildings but also that of urban areas thanks to the use of environmental triggers for their activation that do not require additional energy consumption [12].

Kinetics architecture, on the other side, involves reactivity and movement at the macroscopic scale, since this approach usually refers to the dynamicity of building components [3]. It refers, indeed, to a design concept in which building components can move and transform in response to environmental conditions, user needs, or functional requirements.

Kinetic systems, or responsive components in general, have been used in architecture for thirty years, primarily for aesthetic purposes or environmental control [13], becoming commonplace in contemporary architecture [14]. They integrate mechanical, electronic, and structural systems to create dynamic and adaptable structures that go beyond static functionality.

Kinetic devices can then be described as responsive elements that offer to the architectural design of building envelopes new performance, contributing to thermal comfort, mitigating the environmental impact, avoiding thermal overheating and glare, providing shade, and enhancing illuminance control. Besides adaptability and sustainability, kinetic architecture often incorporates interactive elements that engage users, allowing them to influence buildings' behavior through manual or automated controls. Examples of kinetic devices for building envelopes are systems and components that enable parts of the shell to move or change in response to various stimuli, and range from simple operable components - such as adjustable louvers - to complex systems like movable facades, solar tracking panels, transformable shading systems, retractable roofs and, more in general, transformable space in their whole.

Fig. 4. Eskenazi Hospital parking structure, Rob Ley Studio (2014). Source: Rockinrollblues, CC BY-SA 4.0, via Wikimedia Commons

Fig. 4. Eskenazi Hospital parking structure, Rob Ley Studio (2014). Source: Rockinrollblues, CC BY-SA 4.0, via Wikimedia Commons

As a corollary to the previous paragraphs, it is essential to briefly discuss the matter of sensors and control and actuation devices, which enable the intelligence of buildings, transforming the built environment into an increasingly connected and smart space. Sensors are one of the fundamental technologies supporting the current digitization process and are particularly important for gaining
awareness of the surrounding environment. They provide assistance in terms of security and surveillance while allowing the monitoring of various parameters [15]. The most common use of sensors is to control and manage parameters such as lighting, temperature, and air quality, or to use them for the activation/deactivation of appliances or various devices, as well as to automatically operate building components (e.g., opening or closing windows, adjusting solar shading systems, etc.).

Sensors in use within buildings comprehend:

IoT sensors, used to monitor the environment in terms of "things" such as temperature, motion, light, air quality, and more. The sensors communicate with an access point in the building, and that information is shared with a gateway which gathers all the data and stores it in the cloud, to be accessed when needed.

Analytics software, which, starting from the information gathered by the sensors, show how to turn information into actions to improve building behavior.

User interface, which considers things such as displays, computer screens, and language options that ensure the systems are understood and utilized by buildings' users.
In the end, connectivity is the key factor that allows for it all to be stored, shared, analyzed, and used. All the above components are needed for smart building architecture to provide its full value.

Another alternative that is quickly gaining ground in the construction sector is the use of 3Dprinted materials and components, specifically created for each project and particularly suitable to overcome traditional constraints associated with the construction of complex structures. Through 3D printing indeed, it is possible to create unique shapes that explore intricate geometries using unconventional textures and materials. Printing architecture in 3D means saving on material usage (employing only what is truly necessary for structural strength), thereby reducing environmental impact and related costs. Another fundamental advantage of 3D printing is the speed of execution: the transition from design to construction is practically immediate, allowing for every process to be conceived during the design phase and then directly realized during printing (e.g., the creation of ducts or channels for systems, pipes, and ventilation conduits).

3D printing processes are currently mainly based on stratification (additive manufacturing) even if other techniques exist, including powder-based techniques. However, extrusion processes are more flexible and offer a variety of shapes to suit the geometry. Materials used are primarily cementitious materials/concrete based but include thermoplastics and photopolymer as well, even if their strength properties are weaker; for this reason, they are generally used for rapid prototyping. Some interesting trials are being conducted using nature-based materials (Fig.5), especially for remote housing, to offer fast recovery in post-disaster scenarios.

Fig. 5. WASP (Italy's pioneering specialists in 3D printing) has completed the printing phase of the TECLA supporting structure, the first and unique fully 3D printed construction based on natural materials and made with multiple 3D printers operating at the same time. The innovative habitat model engineered by WASP and designed by MCA - Mario Cucinella Architects was conceived as a new circular model of housing entirely created with reusable and recyclable materials, sourced from local soil, carbon-neutral and adaptable to any climate and context. Source: Alfredo Milano, CC BY 2.5 https://creativecommons.org/licenses/by/2.5, via Wikimedia Commons

Fig. 5. WASP (Italy's pioneering specialists in 3D printing) has completed the printing phase of the TECLA supporting structure, the first and unique fully 3D printed construction based on natural materials and made with multiple 3D printers operating at the same time. The innovative habitat model engineered by WASP and designed by MCA - Mario Cucinella Architects was conceived as a new circular model of housing entirely created with reusable and recyclable materials, sourced from local soil, carbon-neutral and adaptable to any climate and context. Source: Alfredo Milano, CC BY 2.5 https://creativecommons.org/licenses/by/2.5 , via Wikimedia Commons

Generally, construction applications of 3D shapes (e.g. for walls) require reinforcement to improve mechanical properties and the bond between printing layers, placed in different ways according to structural demand (they can be pre-incorporated - either fiber or bars, online incorporated or post processed). Nevertheless, 3D printed concrete can achieve strength results similar to traditionally built elements. Concerning the specific applications of 3D printing to architecture, it is preferable to create non-standard designs because simple shapes (e.g. bricks) are cheaper to manufacture using conventional methods).

Despite the rapid and numerous developments in recent years, 3D printing in buildings is still at an early stage; research remains primarily focused on printability and structural capacity. There is still a gap in physical aspects (such as thermal and acoustic behavior) [16], which could also become an interesting field of investigation, by acting for instance on the element's structure.

Case-Study Analyses

The original version of this paper is available on https://www.scientific.net/EI.14.25.pdf

The previous considerations have allowed to build a matrix for the evaluation and analysis of a sample of architectural case studies considered "smart" according to different points of view. The case studies analyzed come from the whole world, in a period that ranges from the mid-late '90s (1970) till nowadays. Details collected regarding each application are primarily focused on general approaches and working principles, together with data regarding design practice (scale of applications, type, scope, functioning, trigger, adaptive, response, and adaptability), technology readiness, and economic aspects, if available. Some of them presents also deeper insights into challenges, limitations, and future applications of the technologies adopted.

Here following, for synthesis purposes, only one case study considered representative of each category previously presented has been briefly discussed. Common themes, challenges, and opportunities emerging from the case studies' analysis are summarized in the conclusive section of the paper.

BIOMIMETIC | Eye Beacon Pavilion, UNStudio (2017) Amsterdam Light Festival [17]. The Eye Beacon Pavilion is a lightweight, temporary structure designed by UNStudio in collaboration with the textile manufacturer MDT-tex. Its design features a fluid, organic shape that creates an immersive experience thanks to the pavilion's form, inspired by the surrounding marine environment and combining the meaning of a beacon, symbolizing guidance and illumination, with the bioluminescence typical of marine organisms.

Eye Beacon visually represents the festival's theme of connectivity and interaction, using choreographed light sequences to signal its presence to visitors, serving both as an architectural sculpture and an information point for the festival. The cubic forms, constructed from modular components, are connected by parametrically modeled surfaces to optimize the fabrication and installation of the various components. The result is a structure composed of 250 uniquely sized panels made from specially designed tensile textile modules. The overlapping panels reveal glimpses of the interior spaces, displaying LED projections, and give the pavilion an ever-evolving aesthetic.

The pavilion structural framework consists of lightweight aluminum and steel, ensuring stability while allowing for ease of assembly and disassembly. The exterior is clad with translucent panels that diffuse light, creating a glowing effect that enhances the visual impact. LED lights integrated into the structure change color and intensity based on movement and interaction: as visitors move through and around the pavilion, the lights react in real time, fostering a sense of engagement and participation. This interactive lighting system is controlled through a combination of motion sensors and responsive algorithms.

The pavilion exemplifies an innovative fusion of architecture and light art, creating a dynamic and interactive public space that serves as a captivating landmark within the festival, characterized by its striking form, advanced lighting technology, and interactive elements.

SMART MATERIALS | HydroSKIN, Eisenbarth and Haase (2022), University of Stuttgart [18]. Developed by the University of Stuttgart to address the issues of extreme climate events
(specifically, overheating and flooding) in urban surroundings, HydroSKIN is a lightweight textilebased façade element designed to harvest rainwater and utilize evaporation for cooling both building interiors and surrounding urban areas. It is composed of a spacer fabric, consisting of two textile layers separated by threads to ensure adequate ventilation. This structure facilitates efficient air circulation, enhancing the evaporation process and thereby increasing the cooling effect of the façade on which it is applied. The outer layer of the fabric is a water-permeable textile, allowing rainwater to penetrate while blocking insects and debris. An inner film directs the collected water to a lower profile system, where it can be stored in a reservoir or directly used within the building. During hot weather, the water is redirected to the facade element to evaporate, providing a cooling effect. Moreover, thanks to their lightweight, HydroSKIN elements can be retrofitted to any façade, making them a valuable addition to both new and existing buildings.

Enhancing resource efficiency and climate protection in urban areas through the evaporative cooling principle, HydroSKIN mimics the natural cooling provided by parks and green areas, where approximately of rainwater evaporates, compared to only on sealed surfaces like roads and buildings. This innovative system is currently being tested on the world's first adaptive high-rise building at the University of Stuttgart's Vaihingen Campus; preliminary results are promising, with laboratory tests demonstrating a temperature reduction of about due to evaporation.

KINETIC ARCHITECTURE | The Shed, a Center for the Arts (2015), Diller Scofidio + Renfro, New York [19] (Fig.6). The Shed is a groundbreaking example of adaptable architecture that reimagines the traditional arts center. Located in the Hudson Yards area in New York City, it redefines the concept of a cultural space through its innovative design, flexible spaces, and commitment to technological integration. The building indeed allows for various formal transformations to accommodate different public activities thanks to the presence of a retractable roof which permits the physical transformation of the built space, enabling changes in geometry and internal layout to host different artistic visions. The movement of the facade also impacts the immediately adjacent external space.

The building is composed of two primary components: a fixed base structure and a movable shell. The base, a six-level building, houses traditional performance spaces, galleries, and rehearsal rooms. This part of the structure is cladded with a glass and steel facade, allowing natural light to penetrate and providing transparency to the activities within. The most striking feature of The Shed is its telescoping outer shell, which is a steel-frame structure clad in translucent ETFE panels. Advanced acoustics and lighting systems are incorporated throughout the building, ensuring optimal conditions for diverse artistic presentations. This shell can be deployed over the adjoining plaza to create a vast, climate-controlled space known as the McCourt. When extended, the shell offers an additional 17,000 square feet of programmable space, capable of hosting large-scale performances, installations, and events. The movable shell operates on a sophisticated system of rails and wheels, powered by a combination of mechanical and hydraulic systems. This kinetic system is inspired by the industrial past of the High Line and the West Side Railyard. This flexibility is further enhanced by the modular interior spaces, which can be reconfigured thanks to movable walls and stages to host different types of events concurrently.

Fig. 6. The Shed @ Hudson Yards in 2019. Source: Ajay Suresh, CC BY 2.0, via Wikimedia Commons

Fig. 6. The Shed @ Hudson Yards in 2019. Source: Ajay Suresh, CC BY 2.0, via Wikimedia Commons

3D PRINTING | The Striatus bridge (2021), Zaha Hadid Architects in collaboration with incremental3D, Holcim, and Block Research Group, Venice [20, 21]. Striatus is a complex structured concrete bridge entirely 3D-printed, the first of its kind as it stands solely by mutual compression without additional structural reinforcements. This maximizes the properties of the material used (concrete) exploiting its strength while reducing material usage by about compared to similar conventional structures, without compromising performance. Its design indeed, draws inspiration from traditional masonry arches, employing modern computational design to optimize the structure for load-bearing efficiency and material usage. The bridge spans 16 meters and is composed of 53 hollow, 3D-printed concrete blocks. These blocks are printed with an advanced, layered method that ensures precision and structural integrity. The concrete used is a specially formulated, low-carbon mixture specifically developed.

The bridge's form-finding process, based on principles of compression and equilibrium, results in a highly efficient structure that requires no additional support elements. Further, thanks to the homogeneity of the whole components, given the absence of reinforcements, adhesives, or binders, the block can be disassembled and reused indefinitely or easily recycled.

Located in Venice, Italy, the Striatus Bridge was inaugurated during the Venice Architecture Biennale, symbolizing a forward-thinking approach to architectural design and construction. It provides a model for future projects that seek to integrate cutting-edge technology with sustainable practices.

Conclusions and Future Research's Perspectives

The original version of this paper is available on https://www.scientific.net/EI.14.25.pdf

The results of this study try to provide a general overview of the diverse applications' range of advanced materials and technologies within the building envelope domain, counterposed to traditional static design that too often fall short in adapting to the dynamic indoor and outdoor conditions of buildings [22].

The analysis of the vast sample of case studies analyzed, here only very briefly presented for synthesis purposes, so as their categorization, has facilitated a nuanced understanding of the various approaches and techniques employed in the design and construction of smart buildings, revealing common themes and trends among them: above all, the need to control parameters that are responsible of building efficiency and internal comfort in buildings, with a particular attention towards sustainability. On the other side, the examination of economic aspects has highlighted the potential cost implications associated with the adoption of advanced materials and technologies in building design, underscoring the need for careful consideration of cost-effectiveness and long-term value.

Cost control surely represent a challenge; demonstrating the economic viability of advanced building technologies means to promote their wider market adoption and implementation since addressing the cost-effectiveness issue is crucial for the widespread application of smart building envelopes. Although smart envelopes can significantly improve energy efficiency and reduce operational costs, their initial investment and maintenance costs remain major barriers to their widespread adoption [22].

On the other hand, the integration of advanced materials and technologies in building envelopes offers exciting opportunities to enhance the sustainability, efficiency, and resilience of buildings, thereby contributing to the creation of more livable and environmentally conscious urban environments. However, the widespread adoption of smart architecture also poses challenges, including concerns related technological complexity and societal acceptance/awareness of their applications in the current context.

Technical integration challenges that advanced technologies may encounter in practical applications probably requires a conceptual shift toward the understanding that each design is a prototype; so, rather than focusing merely on manufacturing techniques of these materials or on the complexity of the production and installing process, the priority must be to develop the ability to design tailor made solutions, based on individual conceptual model [23].

In parallel, there is the need to push forward research on system integration and interoperability, and to investigate user acceptance and behavioral responses to smart building envelope technologies, designing systems able to better meet user needs [22].

Future directions for extending emerging application area can be raising awareness about specific topics (e.g. media façades) [23] or to engage people even closely, toward an improvement in cities' life quality as well [24].

Moving forward, it is essential for architects, engineers, policymakers, and other stakeholders to collaborate closely to address these challenges and unlock the full potential of Smart architecture. By fostering innovation, promoting interdisciplinary collaboration, and embracing principles of sustainability and inclusivity, it is possible to create buildings and urban environments that are not only smarter but also more resilient, equitable, and responsive to the needs of all members of society.

The findings of this study underscore the transformative potential of smart architecture in shaping the future of the built environment and highlight the importance of continued research and innovation in this field.

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The original version of this paper is available on https://www.scientific.net/EI.14.25.pdf

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M. E. Soliman and S. Bo, "An innovative multifunctional biomimetic adaptive building envelope based on a novel integrated methodology of merging biological mechanisms," Journal of Building Engineering, vol. 76, Oct. 2023, doi: 10.1016/j.jobe.2023.106995.

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Engineering Innovations, ISSN: 2813-1002, Vol. 14, pp 37-44

doi: 10.4028/p-3moDbu

© 2025 The Author(s). Published by Trans Tech Publications Ltd, Switzerland.

Submitted:
2025-01-10

Revised:
2025-01-13

Accepted:
2025-01-17

Online:
2025-04-04

Study of Solar and Wind Energy Potential for Power Generation in Pulo Aceh

The original version of this paper is available on https://www.scientific.net/EI.14.37.pdf

Agus Adria , Ahmad Syuhada , Tarmizi Tarmizi , Ira Devi Sara

Doctoral Program in Engineering Science, Postgraduate Program, Syiah Kuala University, Aceh 23111, Indonesia

agus.adria@usk.ac.id, ahmadsyuhada@usk.ac.id, tarmizi@usk.ac.id, iradevisara@usk.ac.id

Keywords: Wind Energy, Solar Energy, Hybrid Energy, Pulo Aceh.

Abstract

Pulo Aceh is a region located in the westernmost part of Indonesia. The remote location of Pulo Aceh, far from urban areas, makes transportation costs for economic activities more expensive and affects the income of the community. On the other hand, the lack of adequate infrastructure and the long transmission distance make it difficult to establish electricity from the national power company (PLN), resulting in limited electricity supply in Pulo Aceh. This study aims to assess the potential of wind and solar energy in Pulo Aceh as alternative sustainable energy sources. The study was conducted by collecting wind speed and solar radiation data for four months, from January to April. The data was analyzed to determine the energy potential that can be generated from these sources. The research results show that Pulo Aceh has significant potential for harnessing wind energy. The average wind speed during the research period reaches , which is sufficient to drive wind turbines and generate electricity ranging from 319-666 W. Furthermore, the potential of solar energy generated is also promising, with an average solar radiation intensity of throughout the research period. Therefore, the potential of both wind and solar energy can be utilized, either in rotation or in combination (hybrid) form.

Introduction

The original version of this paper is available on https://www.scientific.net/EI.14.37.pdf

The need for energy is very large because technological developments in various sectors are very advanced, with very large needs, thus fuel oil is unable to meet all of these needs. [1] The right step to reduce fuel oil, especially as a power plant, is to utilize renewable energy sources as a power plant. The use of renewable energy has great potential as electrification for rural areas. Generally, solar energy can be utilized in the dry season which lasts about 8 months a year which shines between 10:00 WIB and 15:00 WIB. Meanwhile, wind energy can be utilized if the average wind speed is above with a gust duration of 4-5 hours per day. [2] Seeing the large potential for utilizing both of these energies, this study will review the potential and utilization of renewable energy (solar and wind) in Pulo Aceh, Aceh Besar Regency, Aceh Province.

Solar Energy and Wind Energy

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Solar energy converted into electrical energy is also called photovoltaic energy. Solar modules can take sunlight containing electromagnetic waves (photon energy). Photon energy in the sun creates kinetic energy that can release electrons moving towards the conduction band, causing an electric current. [3]

The energy available in the wind is essentially the kinetic energy of a large mass of air moving over the earth's surface. The blades of a wind turbine that receive kinetic energy will be converted into mechanical or electrical energy, depending on the end use. The efficiency of converting wind energy into other forms of energy use depends on the efficiency of the rotor interacting with the air flow. [4] According to [5] , wind speed measurements should be carried out at a height of 10 meters above ground level. However, potential wind speeds can be much higher at higher heights.

Fig. 1. Field of air moving towards a wind turbine.

Fig. 1. Field of air moving towards a wind turbine.

Source: Sathyajith Mathew [4]
To determine the amount of power generated from the air field on the rotor, you can use the formula:

The factors that affect the power available in the air flow are air density, wind rotor area and wind speed. [4]

Hybrid energy systems consist of two or more renewable energy sources used simultaneously to increase system efficiency and promote greater balance in energy supply. [6]

Research Methods

The original version of this paper is available on https://www.scientific.net/EI.14.37.pdf

This research was conducted for 3 months in Melingge Village, Pulo Aceh District, Aceh Besar Regency, and Ulee Lheue Village, Banda Aceh City. Data processing was conducted at the Thermal Engineering Laboratory, Mechanical Engineering Study Program, Department of Mechanical and Industrial Engineering, Faculty of Engineering, Syiah Kuala University.

The process of collecting solar radiation intensity data is carried out using a Luxmeter measuring instrument. The measuring instrument is placed in an open field with solar radiation that is not blocked by buildings, trees or clouds. The process of collecting wind data is carried out using an Anemometer measuring instrument at a predetermined location point with a height of 10 meters and wind speed is recorded every hour.

Primary wind speed data taken from measurement data conducted every hour from 7:00-22:00 WIB, by comparing the values between the two location points, if the values obtained are the same then the measurement is only carried out at one point. While for solar radiation measured at 10:00-16:00WIB, measurements were carried out from January-April. While for secondary data taken from BMKG Aceh Besar which has been going on for the last 3 years every month and every day.

Results and Discussion

The original version of this paper is available on https://www.scientific.net/EI.14.37.pdf

The data obtained from the measurements are data measured daily, weekly, and monthly. While the annual data as a comparison of data is taken from BMKG Aceh Besar.

1. Analysis of Daily Measurement Results

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The results of measurements carried out from WIB taken over three days, the average wind speed can be seen in Figure 2.

Fig. 2. Hourly wind speed distribution.

Fig. 2. Hourly wind speed distribution.

Figure 2 shows data taken over three days, it can be seen that the wind speed on the red line is the wind speed that has the potential to generate electrical energy, with wind speeds ranging from starting at 14.00 to 19.00 WIB. According to [2] the minimum wind speed that can be used to generate electricity is . While the wind speed below 15.00 WIB is only around and above 18.00 WIB the wind speed decreases again, so that wind speeds below cannot be used to generate electricity.

The results of solar radiation intensity measurements measured at WIB can be seen in Figure 3.

Fig. 3. Hourly solar radiation intensity.

Fig. 3. Hourly solar radiation intensity.

The intensity of solar radiation that occurs at 7:00-19:00 WIB shows that the highest solar radiation intensity value is at 12:00-13:00 WIB, which is . The solar radiation intensity value looks quite high starting at 10:00 WIB, which is to 16:00 WIB, which is . While at 17:00-19:00 WIB. According to [7] the intensity of solar radiation can be utilized at a minimum value of , from the results obtained, generally the solar intensity value that occurs is very optimal when conditions are sunny, but at certain times the solar intensity value is obtained which is not optimal due to the movement of clouds that cover the sun so that the optimal time for sunlight occurs is faster.

2. Analysis of Weekly Measurement Results

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The results of measurements every day of every week obtained the average wind speed value which can be seen in Figure 3.

Fig. 4. Weekly highest average wind speed.

Fig. 4. Weekly highest average wind speed.

The analysis in Figure 3 shows the highest weekly average wind speed during a 6-hour period, from 14:00-19:00 WIB. Based on the figure, it can be observed that the wind speed that occurs every day is relatively the same and stable, with a value above .

The constant and stable wind speed above in that time span indicates good potential for wind energy utilization in that location. High wind speeds like this allow the use of wind turbines with high efficiency in generating electrical energy.

The results of daily measurements in each week on the average solar radiation intensity value from 10.00-16.00 WIB can be seen in Figure 4.

Fig. 5. Weekly highest average solar radiation intensity.

Fig. 5. Weekly highest average solar radiation intensity.

Data analysis shows that the intensity of solar radiation measured daily between 10:00 and 16:00 WIB shows stability and relatively consistent values. The intensity of solar radiation observed daily reaches a figure above , indicating high radiation levels and significant solar energy potential at the location.

However, it is important to note that this analysis is based on data collected over a specific time period and may not capture all variations that may occur at a given location. More extensive data collection and more in-depth analysis are needed to ensure the accuracy and appropriateness of the information regarding solar energy potential at a specific location.

3. Monthly Measurement Results Analysis

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The results of wind speed measurements for each month starting from January to April can be seen in Figure 5.

Fig. 6. Monthly highest average wind speed.

Fig. 6. Monthly highest average wind speed.

Wind speed measured from January to April with the highest average wind speed measurement process starting at 14.00-19.00 which lasted for approximately 6 hours and the results stated that for 4 months the wind speed was very feasible to be utilized. Although the wind speed shows a potential that is feasible to be utilized, it is important to remember that wind speed can vary and is not always optimal all the time. Wind speed can experience daily, seasonal, or even annual fluctuations, depending on factors such as weather conditions, topography, and local wind patterns.

The results of solar intensity measurements during the period from January to April can be found in Figure 6. The figure presents data on the intensity of solar radiation recorded each month.

Fig. 7. Average highest monthly solar radiation intensity.

Fig. 7. Average highest monthly solar radiation intensity.

Through the analysis of the data in Figure 7, it can be seen that the intensity of solar radiation that occurs each month remains within a range that is feasible to be utilized. This shows that even though there are fluctuations in the intensity of solar radiation throughout the months, the potential for solar energy still exists and can be used effectively. However, it should be noted that the data presented in Figure 7 only reflects the results of measuring the intensity of solar radiation and does not consider other factors that can affect the utilization of solar energy.

4. BMKG Wind Speed Data Analysis

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The wind speed data obtained is data from 2020 to 2022, the wind speed values can be seen in Figure 8.

Fig. 8. BMKG wind speed data.

Fig. 8. BMKG wind speed data.

Wind speed data obtained from BMKG Aceh Besar shows that the wind speed that occurs is relatively the same, ranging from , the wind speed recorded by BMKG can be categorized as a decent result and has the potential to produce electrical energy that can be utilized every month.

5. BMKG Solar Radiation Intensity Data Analysis

The original version of this paper is available on https://www.scientific.net/EI.14.37.pdf

The solar radiation intensity data obtained is data from 2020 to 2022, the solar radiation intensity value can be seen in Figure 9.

Fig. 9. BMKG solar radiation intensity data.

Fig. 9. BMKG solar radiation intensity data.

Solar radiation intensity data shows that the average value for the last 3 years has relatively no significant difference, the visible solar radiation intensity value is always above . The solar radiation intensity value for each month is not much different, thus the overall solar energy potential can still be utilized for each month.

6. Comparative Data Analysis

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Comparison of wind speed and solar radiation intensity at the research location and BMKG data from 2020 to 2023 can be seen in Figure 10.

Fig. 10. Comparison of wind speed data.

Fig. 10. Comparison of wind speed data.

In Figure 10, it can be seen that the measured wind speed has a not too significant difference, where the wind speed obtained from BMKG ranges from while the wind speed obtained from direct measurements ranges from . This difference in value occurs because of the difference in the location of the data collection point carried out by BMKG and there are differences in the data collection methods carried out by BMKG.

Fig. 11. Comparison of solar radiation intensity data.

Fig. 11. Comparison of solar radiation intensity data.

Figure 11 shows that the measured solar radiation intensity values have a difference in value that is not much different between the data obtained from BMKG and data based on measurements, where the data obtained from BMKG ranges from , while the data based on measurements ranges from . This difference can be caused by differences in data collection methods carried out by BMKG.

7. Wind Energy Potential

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Based on the data obtained, it is known that the average wind speed in Pulo Aceh is above with a total time of approximately 6 hours a day. Based on research conducted by [8], it is stated that by using a horizontal propeller-type wind turbine with five blades and a blade length of 100 cm paired with a 1000 W generator using a 2:10 transmission, it can generate electricity starting at a wind speed of . The turbine is capable of rotating at a speed of 400 rpm and can generate 50 W of electrical power. The power that can be generated by wind speed can be calculated using equation (1) which can be seen in Figure 12.

Fig. 12. Average wind power.

Fig. 12. Average wind power.

Figure 12 shows that the average wind power obtained with data measured over 4 months, the largest power gain was 666.88 W in April, while the smallest power was in January at 319.65 W . The power obtained is sufficient to meet electricity needs (small scale). [9]

8. Solar Energy Potential

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Based on the data obtained, Pulo Aceh has a fairly high intensity of solar radiation which lasts for approximately 7 hours starting from 10:00 WIB to 16:00 WIB which has a solar radiation intensity above . The power generated each month can be seen in the following image.

Fig. 13. Average solar radiation intensity.

Fig. 13. Average solar radiation intensity.

Figure 13 shows that the potential for solar energy in the Pulo Aceh area is very adequate so that it has important potential to provide electrical energy that can be used simultaneously with wind energy, and can be used alternately if on certain days there is no wind potential at all, so that solar energy can be used as a source of electrical energy. [10]

This shows that Pulo Aceh has the appropriate parameters for building solar, wind or hybrid power plants (a combination of solar and wind energy).

Conclusion

The original version of this paper is available on https://www.scientific.net/EI.14.37.pdf

Based on the results of the research that has been conducted, several conclusions can be drawn as follows:

Wind speed in Pulo Aceh reaches significant potential with an average wind speed of , with a duration of approximately 6 hours from 14.00 to 19.00 WIB with a power of in January to April.

The solar intensity starting from January to April has an average solar radiation intensity of This provides a great opportunity to utilize solar energy as a powerful source of electricity.

The use of solar power as an energy source can be used during the day, and at night, electricity can be used from wind turbines that have been stored.

References

The original version of this paper is available on https://www.scientific.net/EI.14.37.pdf

M. Syukri, "Perencanaan Pembangkit Listrik Tenaga Surya (PLTS) Terpadu Menggunakan Software PVSYST Pada Komplek Perumahan di Banda Aceh," J. Rekayasa Elektr., vol. 9, no. 2, pp. 77-80, 2010.

A. Syuhada, M. I. Maulana, and Z. Fuadi, "Analysis of Wind Energy Potential For Agriculture Pump in Mountain Area Aceh Besar," AIP Conf. Proc., vol. 1855, no. June 2017, 2017, doi: 10.1063/1.4985534.

I. Rochimawati, "Perancangan Pembangkit Listrik Tenaga Surya," Strateg. J. Tek. Ind., vol. 1, no. 1, pp. 169-180, 2019, doi: 10.37753/strategy.vli1.7.

S. Mathew, Wind Energy: Fundamentals, Resource Analysis and Economics. Malapuram: Springer, 2007. doi: 10.1007/3-540-30906-3.

WORLD METEOROLOGICAL ORGANIZATION, Guide to Meteorological Instruments and Methods of Observation, WMO-No. 8., no. WMO-No. 8. Geneva Switzerland: Secretariat of the World Meteorological Organization, 2006.

Z. Tharo and M. Andriana, "Pembangkit Listrik Hybrid Tenaga Surya Dan Angin Sebagai Sumber Alternatif Menghadapi Krisis Energi Fosil Di Sumatera," Semnastek UISU, vol. 2, no. 4, pp. 141-144, 2019.

Bayuaji Kencana et al., "Panduan Studi Kelayakan Pembangkit Listrik Tenaga Surya (PLTS) Terpusat," Indones. Clean Energy Dev. II, no. November, p. 68, 2018.

A. Syuhada, M. I. Maulana, Syahriza, M. S. M. Sani, and R. Mamat, "The potential of wind velocity in the Banda Aceh coast to the ability to generate electrical energy by horizontal axis wind turbines," IOP Conf. Ser. Mater. Sci. Eng., vol. 788, no. 1, p. 012082, Apr. 2020, doi: 10.1088/1757-899X/788/1/012082.

D. Ginting, "Sistem Energi Angin Skala Kecil Untuk Pedesaan," J. Ilm. Teknol. Energi, vol. 1, no. 5, pp. 40-51, 2007.

S. D. Prasetyo and A. Ulinuha, "Rancang Bangun Pembangkit Hybrid Tenaga Angin dan Sel Surya," Universitas Muhammadiyah Surakarta, 2018.

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The original version of this eBook is available on https://www.scientific.net/book/engineering-innovations-vol-14/978-3-0364-1777-6

Engineering Innovations, ISSN: 2813-1002, Vol. 14, pp 45-52

doi: 10.4028/p-F5yoGf

© 2025 The Author(s). Published by Trans Tech Publications Ltd, Switzerland.

Submitted:
2024-12-30

Revised:
2025-01-13

Accepted:
2025-01-21

Online:
2025-04-04

Challenges in Water Energy Development for Energy Security in Aceh

The original version of this paper is available on https://www.scientific.net/EI.14.45.pdf

Razali Thaib , Zulfan , Ichsan Setiawan , Ayana Rizki

1Department of Mechanical Engineering, Faculty of Engineering, Universitas Syiah Kuala, Aceh, Indonesia

Department of Marine Science, Marine Science and Fisheries Faculty, Universitas Syiah Kuala, Darussalam, Banda Aceh. Indonesia.

Fishing and Navigation Vessel Laboratory, Marine and Fisheries Faculty, Universitas Syiah Kuala, Darussalam, Banda Aceh. Indonesia.

razalithaib@usk.ac.id, zulfanstmt@usk.ac.id, dayanarizki1@gmail.com

*razalithaib@usk.ac.id

Keywords: renewable energy, great potential, water energy

Abstract

Aceh has great potential for the development of hydropower as a sustainable energy source. However, a number of challenges must be overcome to realize this potential and ensure energy stability in the region. The study was conducted through interviews using questionnaires, and literature studies from various sources. The main causes are deteriorating infrastructure, transportation difficulties, local technology and education problems, and environmental damage due to energy infrastructure development. In addition, weak regulations, varying community standards and limits, climate change, and political stability and security are obstacles to energy development in Aceh. The purpose of this study is to identify and analyze these challenges and provide strategic recommendations to mitigate the associated hazards. Hydropower development can be optimized to support energy security in Aceh with the government, private sector, local communities, and experts taking a collaborative approach.

Introduction

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The potential for hydropower in Aceh is very large considering the geographical conditions, high rainfall, and the existence of large rivers that have the potential for the construction of Hydroelectric Power Plants. Most countries aiming to replace traditional energy sources with renewable ones have begun making large investments in this field. Since renewable energy requires significant investment, evaluating investment risks is crucial for making sound decisions [1]. Factors such as technological developments, industrialization, and population growth have caused the world's energy demand to continue increasing, while available resources are insufficient to meet these needs. Consequently, the search for alternative energy sources has accelerated, leading many countries to focus on renewable energy. In addition to research and development in new renewable energy technologies, building an effective socialization program is essential. This requires a solid decision-making framework, which this study provides by establishing criteria and evaluating their importance using the fuzzy analytical hierarchy process (AHP) method [2].
Renewable energy is one of the most popular alternatives to traditional energy sources. In Indonesia, renewable energy is available in the form of solar power, hydropower, geothermal energy, wind energy, and biomass. Each type of renewable energy has its own advantages and disadvantages, making it important to select the most appropriate source to maximize benefits. A selection methodology based on fuzzy AHP is proposed, which introduces a novel procedure for gathering expert opinions. Several selection criteria suitable for Indonesia are also presented. According to the analysis, hydropower is the best renewable energy source, followed by geothermal, solar power, wind power, and biomass [3].
The challenges in developing hydropower for energy security in Aceh are highly relevant, given the region's abundant water resources. With numerous rivers, lakes, and high rainfall, Aceh has
significant potential to utilize hydropower as a solution for its energy security. However, this potential has not yet been fully realized, and Aceh continues to face major challenges, including a dependence on fossil fuels and an unstable energy supply. These issues underscore the urgency of developing renewable energy, particularly hydropower, which could play a crucial role in addressing the region's energy crisis.
On the other hand, the development of hydropower infrastructure in Aceh faces various obstacles. One major challenge is the limited technology and the high investment costs required to build the infrastructure, particularly in remote areas that are difficult to access. In addition to technical challenges, hydropower projects also pose environmental concerns, such as their impact on aquatic ecosystems and the risk of land use changes, which can affect local communities. Moreover, while renewable energy policies in Indonesia are still evolving, they are often insufficient to support the acceleration of hydropower development. As a region with special autonomy, Aceh also faces regulatory complexities in developing energy projects, including hydropower. Another significant challenge is the shortage of human resources and financing. Hydropower development requires experts and substantial funding, both of which remain significant obstacles in Aceh. However, the development of hydropower in Aceh is a crucial step toward achieving sustainable energy security. By diversifying energy sources and reducing dependence on fossil fuels, Aceh can establish a more stable and environmentally friendly energy supply. Therefore, it is essential to address these challenges to ensure that the potential of hydropower is fully harnessed, thereby strengthening energy security in the region.

Research Methods

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This research was conducted in several districts and cities in Aceh Province. The subjects of this study included the general public of Aceh, as well as several academics and experts in the field of renewable energy, together with companies and NGOs involved in the oil energy sector, to collect information related to a number of challenges that must be overcome to realize this potential and ensure energy stability in the Aceh region. This study involved 150 respondents. A sample size of 150 people is often considered representative enough for exploratory studies or surveys. Primary data were collected through structured interviews using questionnaires, which captured respondents' opinions on a number of challenges that must be overcome. The selection of respondents for interviews was carried out using the purposive sampling method. Secondary data were collected from previous studies, journals, and other publications relevant to this study.

Result and Discussion

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Aceh, a province rich in natural resources, faces significant challenges in its efforts to achieve sustainable energy security, particularly from renewable energy sources. Despite its immense potential to harness clean energy, such as hydropower, wind, and solar, various obstacles hinder Aceh from fully optimizing this potential. These challenges include limited infrastructure, difficulties in financing, slow adoption of technology, and complex social and environmental issues. Furthermore, political and security dynamics, along with the impacts of climate change, add additional layers of complexity to the efforts to build a resilient energy sector. In this context, understanding and addressing these challenges are crucial for Aceh to ensure the sustainability of its future energy supply while fostering inclusive and environmentally friendly economic development. Below is a breakdown of the obstacles Aceh faces in achieving sustainable energy security (see figure 1).

Fig. 1. Results of mapping challenges in Water Energy Development for Energy Security in Aceh.

Fig. 1. Results of mapping challenges in Water Energy Development for Energy Security in Aceh.

a. Infrastructure Challenges

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Energy infrastructure in Aceh still faces significant limitations, especially in remote and hard-to-reach areas [4]. One of the main issues is the uneven availability of electricity networks, with many rural areas and small islands still not well connected to the national grid. This forces people in these areas to rely on alternative energy sources, which are often inadequate and unstable, such as diesel generators that require expensive and hard-to-access fuel [5]. Additionally, fuel distribution in Aceh faces major challenges. Limited access to remote areas makes fuel delivery difficult and expensive, leading to inconsistent energy supplies. This issue is exacerbated by the lack of energy storage facilities, making fuel distribution inefficient and vulnerable to supply disruptions. The limitations in energy transportation-both electricity and fuel-add to the complexity of the problem, given Aceh's geographical terrain, which includes mountains, dense forests, and long coastlines.
To address these challenges, it is essential for Aceh to develop better and more reliable energy infrastructure. The construction of more efficient and environmentally friendly power plants, along with the expansion of the electricity distribution network to cover the entire region, are critical steps that must be taken. Additionally, strengthening energy storage facilities [6] for both electricity and fuel [7] is necessary to ensure better and more stable energy management. With adequate infrastructure, Aceh can improve access to and availability of energy, ultimately driving economic growth and enhancing the quality of life for people across the province.

b. Funding Challenges

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Financing is one of the biggest challenges facing energy project development, particularly in a region like Aceh, which requires significant investment to upgrade its energy infrastructure and technology. Energy projects are often extremely costly, especially when they involve high-tech applications and complex infrastructure development [8]. Additionally, many of these projects are long-term, meaning that investments do not yield immediate returns, making investors hesitant to commit. This is particularly relevant in Aceh, where economic and political uncertainty can influence investor interest.
Budget constraints faced by local governments are also a major obstacle. While the central government may have larger financial resources, these funds are often not fully allocated to energy
projects in more remote or less developed areas. Local governments in Aceh, with their limited resources, struggle to finance large-scale projects, particularly those requiring substantial upfront investment before any economic benefits are realized. This situation is further exacerbated by the lack of alternative revenue sources and fiscal constraints, which limit the ability of local governments to support energy development [9].

c. Lack of Technology

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Aceh continues to face various limitations regarding the mastery and adoption of energy technology, encompassing both the renewable and non-renewable energy sectors. These limitations not only affect the effectiveness of existing energy resource utilization but also hinder the overall development of the energy industry in the region. The technology currently available is often outdated, failing to keep pace with modern advancements, or is inadequate for meeting increasingly complex needs. For instance, existing power plants may still rely on old technology that has low efficiency and high emissions, rendering them not only less economical but also detrimental to the environment.
In the renewable energy sector-such as solar, wind, and biomass-the adoption of technology also lags behind. Although Aceh possesses great potential for developing renewable energy, the technology required to convert this potential into usable energy resources remains limited. For example, the solar panel and wind turbine technologies currently in use may not be efficient enough to generate large amounts of energy or withstand harsh environmental conditions. As a result, the utilization of renewable resources in Aceh is suboptimal, leading to significant energy potential being wasted.
These limitations are also connected to a lack of knowledge and skills among the local workforce in operating and maintaining more advanced energy technologies. Many workers may not have received adequate training in the latest technologies, which means that even when new technologies are adopted, their use is not always maximized. This lack of capacity further slows down the technology adoption process, as additional time and resources are needed to train the local workforce.

d. Human Resource Expertise

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The lack of skilled and professional personnel in the energy sector is one of the main obstacles hindering the implementation and management of energy projects in Aceh. This challenge is critical, given that energy projects typically require high technical expertise, effective management, and a deep understanding of applicable technologies and regulations. However, Aceh currently faces a significant shortage in both the number and quality of competent human resources in the energy sector. These limitations include a scarcity of engineers with specialized expertise in energy and a shortfall in the number of trained technicians. Technicians are essential to the day-to-day operations of energy infrastructure, as they are responsible for running, maintaining, and repairing various systems and equipment. The absence of experienced and trained technicians means that many technical issues that arise in daily operations cannot be resolved quickly or efficiently, leading to significant downtime or even permanent damage to the energy infrastructure.
To address this challenge, developing human resource capacity is crucial. This can be achieved through various strategies, including enhancing formal education in energy engineering at universities and other educational institutions in Aceh. These educational programs need to be tailored to meet the demands of the modern energy industry, with curricula that cover the latest technologies, regulations, and best practices in the energy sector. Additionally, ongoing training is essential to ensure that the workforce can continuously improve its skills as technology advances.

e. Environmental Impacts

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Although renewable energy sources such as solar, wind, and hydroelectric power are considered more environmentally friendly than fossil fuels, their development is not without environmental impacts. For instance, the construction of wind turbine farms can disrupt the migration patterns of birds and bats, as well as alter the natural landscapes that are vital for various species. Similarly, the construction of hydroelectric power plants can lead to changes in river flows, which in turn can disrupt
aquatic ecosystems, reduce fish populations, and destroy wetlands essential for biodiversity. Additionally, the production of solar panels and batteries requires raw materials that are often obtained through environmentally damaging mining practices [10].
These impacts illustrate that energy development cannot proceed without careful consideration of the environment. This necessitates the implementation of strict, environmentally friendly policies that must be integrated into every stage of energy development, from planning and construction to operation. These policies should include comprehensive environmental impact assessments, the use of clean technologies that minimize emissions and waste, and mitigation measures designed to reduce negative impacts on ecosystems. Moreover, mitigation efforts must be an integral part of every energy project. Such efforts can encompass a variety of actions, including rehabilitating degraded land, improving waste management, and employing emission-reducing technologies. For example, in fossil-fueled power projects, the use of carbon capture and storage (CCS) technology can help reduce emissions released into the atmosphere. In renewable energy projects, appropriate site selection and environmentally friendly design can help minimize disturbances to natural [11]. Furthermore, compensation and rehabilitation programs, such as replanting degraded forests or restoring river ecosystems, can help restore the environmental balance disrupted by energy development.
Overall, the environmental challenges faced in energy development in Aceh require a holistic and sustainable approach. The government, industry, and communities need to collaborate to ensure that energy development not only meets current energy needs but also considers its impacts on the environment and future generations. With the right policies, environmentally friendly technologies, and effective mitigation efforts, Aceh can develop its energy sector sustainably, preserving its natural environment while continuing to support the welfare of its people [12].

f. Regulation

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Unclear or overlapping regulations often pose a significant obstacle to the licensing and implementation of energy projects, and this issue is particularly pronounced in Aceh. The regulatory process, which is intended to govern and ensure that energy projects are executed in accordance with standards and the public interest, frequently becomes a substantial barrier for industry players and investors. One of the main challenges faced is the lack of harmonization between central and regional regulations. Aceh, with its special autonomy status, has regional regulations that sometimes differ from or even conflict with national regulations. Additionally, unclear regulations are often related to varying interpretations of the law by the different agencies involved. In many cases, there is overlapping authority among various government agencies, both at the central and regional levels, each of which has different perspectives or approaches to the implementation of energy projects. Slow bureaucracy is also a significant inhibiting factor; the process, which requires numerous stages and approvals from various agencies, can take months or even years before the necessary permits are obtained.

g. Social Acceptance

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One of the main issues that often triggers community resistance is displacement. Energy projects, especially large-scale ones such as power plants or mining operations, frequently require extensive areas of land. This can mean that people living in the area may have to be relocated from their homes, leading to dissatisfaction and resistance. For many communities in Aceh, land is not only viewed as an economic asset but also holds deep cultural and spiritual significance. This displacement is often perceived as a violation of their rights, especially if the process is conducted without adequate consultation or fair compensation.
Additionally, access to natural resources is also a sensitive issue. In many areas of Aceh, communities depend on natural resources for their livelihoods, such as agriculture, fisheries, and forestry. Concerns about environmental impacts often drive community resistance as well. To overcome these challenges and gain better social acceptance, it is essential for the parties involved in energy projects to build trust with the communities [13]This can begin with early community involvement in the project planning process. Engaging communities in discussions and consultations can help them understand
the goals and benefits of the project and provide them with the opportunity to voice their concerns and expectations. This process not only increases transparency but also gives communities a sense of ownership, which can reduce resistance and enhance support [14].

h. Climate Change

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Climate change is a global challenge increasingly felt in various parts of the world, including Aceh. This phenomenon not only affects ecosystems and daily life but also has serious implications for energy security in the region. In Aceh, climate change has caused significant alterations in weather patterns, including erratic rainfall, an increased frequency of storms, and extreme temperatures that occur more often than before. All of these changes have a direct impact on energy production and distribution, especially from renewable energy sources that are highly dependent on natural conditions, such as hydroelectricity and wind energy.
Hydroelectric power sources, which harness the flow of water from rivers or dams, are highly susceptible to changes in rainfall patterns. In Aceh, which has a large number of hydroelectric power plants, the dependence on stable rainfall is very high. When rainfall becomes erratic, for example during prolonged periods of drought or heavy rains that cause flooding, the electricity production capacity of hydropower plants can drop dramatically. Droughts can reduce the flow of water needed to drive turbines, while floods can damage infrastructure and force temporary shutdowns to prevent further damage. This of course has an impact on energy availability, which can be unstable and unpredictable.
Adaptation to climate change must also be a priority in the planning and management of energy infrastructure. Governments and industry need to develop comprehensive adaptation strategies, including planning for infrastructure that is resilient to extreme weather events and developing early warning systems to mitigate the impacts of natural disasters on energy infrastructure. For example, constructing dams designed to withstand major floods or strengthening electricity distribution networks to endure strong winds could be part of these adaptation efforts. Furthermore, it is essential to develop policies that support innovation and investment in technologies that can enhance the resilience of energy systems to climate change [15].

i. Security and Political Stability Issues

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Political security and stability are crucial factors in the success of energy sector development in Aceh. As a region with a history of conflict and complex political dynamics, Aceh requires a stable environment to ensure the sustainability of energy projects that are currently being developed or will be in the future. Conflicts, whether internal, such as disputes between political groups or communities, or external, such as threats from armed groups, can hinder various stages of energy projects, from planning to operations. Political instability can create uncertainty for investors and project developers, ultimately leading to project delays or cancellations.
Creating a stable and secure environment is not only the responsibility of the government but also requires the active participation of various stakeholders, including the community, security forces, and energy industry players. The government must ensure that the policies implemented support political stability and security by creating a clear legal framework, enforcing the law fairly, and facilitating dialogue between groups with differing interests. Communities must also be empowered to play a role in maintaining security, for example, through community security programs or partnerships between communities and security forces [16].

Conclusion

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Aceh faces complex challenges in its efforts to achieve resilience in renewable energy resources. Despite its significant potential in harnessing clean energy sources such as hydro, wind, and solar, several major barriers continue to hinder the optimization of this potential. Infrastructure limitations, difficulties in obtaining adequate funding, slow adoption of new technologies, and a shortage of renewable energy experts are some of the main factors obstructing the development of this sector. Additionally, social issues such as low public understanding of renewable energy, complex regulatory
challenges, and political and security conditions that are not yet fully stable further complicate the situation. The impacts of climate change also add to the burden of ensuring sustainable energy resilience in the region.

Acknowledgments

The original version of this paper is available on https://www.scientific.net/EI.14.45.pdf

The author would like to thank LPPM USK for funding research under the 2024 professor research scheme with contract number 557/UN11.2.1/PG.01.03/SPK/PTNBH/2024.

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Engineering Innovations, ISSN: 2813-1002, Vol. 14, pp 53-59

doi: 10.4028/p-3CcX2q

© 2025 The Author(s). Published by Trans Tech Publications Ltd, Switzerland.

Submitted:
2024-12-23

Revised:
2025-01-16

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Online:
2025-04-04

Analysis of the Effect of Discharge Needs on Pesangan Hydropower Plant in Central Aceh Regency

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Salsabila Ashmalia Putri , Fadhliani , Teuku Mudi Hafli , Adzuha Desmi , Nanda Savira Ersa

Department of Civil Engineering, Malikussaleh University, North Aceh, Aceh, Indonesia

salsabilaashmalia@gmail.com, fadhliani@unimal.ac.id, enanda.savira@unimal.ac.id

Keywords: Hydropower plant, water needs, discharge analysis

Abstract

The Peusangan Hydropower Plant has a power capacity of , or 88 MW from two turbines. In the context of hydropower, turbine requirements include high efficiency, capacity, appropriate power, reliability over an extended period of time, operational flexibility, ease of maintenance, and environmental considerations such as natural habitat. Discharge requirements are a significant factor that must be considered in the planning and operation of hydropower plants. In the event that the discharge requirements are not met, the hydropower plant may be damaged and unable to operate as intended. The objective of this study was to ascertain the discharge requirements of the four sectors surrounding the river and the discharge requirements for the continued availability of the Peusangan Hydropower Plant. This research process was conducted through the analysis of MHP parameters, water needs, discharge scenarios, and zoning scenarios. The results of the analysis of the effect of discharge requirements on Peusangan Hydroelectric Power Plant indicate that the discharge requirements for the four sectors in the river are divided into three. In the 500 m zoning, the required discharge is . In the 1 km zoning, the required discharge is , while in the 2 km zoning, the required discharge is . The minimum discharge requirement for the Peusangan Hydroelectric Power Plant is . The minimum discharge requirement for Peusangan Hydroelectric Power Plant in the 500 m zoning is , while the maximum discharge is . In the 1 km zoning, the minimum discharge requirement is . The maximum discharge is , while the minimum discharge in the 2 km zoning is , with a maximum of . These values ensure that the discharge requirements for hydropower can be met properly.

Introduction

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Water is a basic need for the life of living things on earth, both for direct needs such as drinking water, industrial water, sanitation, and indirect needs such as livestock, irrigation, and power generation. Hydropower Plant or known as PLTA is an electrical energy generation system that can convert potential energy into energy by turbines. The increasing number of people in Indonesia, resulting in the increasing need for electricity which is getting bigger every day. This is because the need for electricity is increasing in the community [2]. The Peusangan hydropower development is located along the Peusangan river in the northwestern part of the Tawar Lake in Aceh, about 320 km southeast of Banda Aceh. Its power capacity is or 88 MW from two turbines. One of the devices used to convert water into mechanical energy is a turbine. Appropriate discharge requirements will ensure that the hydropower plant can operate optimally and produce electricity with high efficiency. In addition, discharge requirements also affect the stability of hydropower plants in the long term. If the discharge requirement is not met, the hydropower plant can be damaged and cannot operate properly. This research will focus on the impact of river needs for various sectors and discharge requirements on the stability and operational efficiency of hydropower plants, as well as explore methods to determine the optimal discharge requirements and overcome the problem of discharge shortages that may occur at Peusangan Hydropower Plant. This study hypothesis that the variations in discharge requipment at different zoning distance have a significant impact on the operational
effeciency and environmental sustainability of the Peusangan Hydropower Plant. The research also aims to identify optimal discharge management strategies to ensure both community water needs and plant operations are sustainably.

Literature Review.

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Conventional Hydropower. The most common form of hydropower plant. Conventional hydropower usually consists of a dam built on a river or lake to settle water, a turbine that converts water energy into mechanical energy and a generator that converts mechanical energy into electrical energy.

Mini Hydropower. a smaller form of hydropower plant compared to conventional and micro hydropower. Mini-hydropower is usually used to supply electricity to communities or individuals, and is often built in areas that are not connected to the national grid. Mini hydropower consists of a small dam or reservoir, a turbine and a generator.

MHP. Micro Hydro Power Plant is a power plant that comes from renewable energy because the availability of water will remain with the condition of the ecosystem maintained. MHP can also be called an environmentally friendly technology because of its non-polluting system. Water that flows with a certain capacity through a pipe, then the water will hit the turbine so that it will produce mechanical energy in the form of rotating the turbine shaft. The rotation of the turbine shaft will rotate the generator so that electrical energy is produced [1]. The efficiency or performance of a turbin is not fixed in value, depending on the load conditions and the type of turbine. Teh performance of a tuebine can be expressed in several conditions, namely, maximum plunge height, minimum plunge height, normal plunge height and design plunge height. At he design heiht the turbine will give its best speed so that its effeciency reaches its maximum. [3]

Water Power. The power entering the francis turbine is the potential power of the water.

Where :
Water hydraulic power (Watt)
P = Density of water ( )
Gravity acceleration
Q = Mass flow rate ( )
Head of falling water height
Turbine Exit Power. The power released by the turbine is shaft power due to the purpose of the turbine which converts kinetic energy into mechanical energy.

Where :
P Mechanical power (Watt)
Turbine rotation speed (rpm)
Torque (Nm)
Turbine Efficiency. Turbine efficiency is obtained from comparing mechanical power with water power.

Where :
Turbine efficiency
Phid = Water hydraulic power (Watt)
Ptur = Water power (Watt)
Domestic Water needs. To calculate the need for clean water, population data is needed and then multiplied by the standard clean water requirement per liter per person per day of 144 liters / day [3].

Where :
Clean water needs (liters/ day)
Pn = Total population in year n (people)
Q = Clean water needs (liters/person/day)
Non-Domestic Water Needs. To calculate non-domestic water needs, the following formula is used: [5]

Where :
Clean water needs (liter/day)
Pf Number of Facilities
Ap = Assumed usage (person/unit)
Q = Discharge (liter/person/day)
Water Requirement for Cages. Based on the river water level elevation of 1,229.9 m which is the minimum elevation value maintained in the river. Some factors that need to be considered in determining the water requirement for floating net cages consist of; water quality, water quantity, water flow, water source, and water level. The depth of the cage used is about with a size of .

Discharge Scenario. Discharge scenarios are conducted to calculate or predict the amount of water flowing through a channel or specific area. Discharge scenarios involve calculating the amount of water needed to meet the needs of the community or industrial activities. Before calculating the discharge scenario, the cross-sectional area of the regulating weir must be calculated using the following formula:

Where:
A = Cross-sectional area (m2)
Basic channel width (m)
m = Channel slope
Water level (m)
To calculate the discharge scenario used 2 comparisons with the minimum and maximum flow velocity using the following formula:

Where:
Q = Regulating weir discharge
Q1 = Discharge of school needs
Q2 = Discharge of prayer room needs
Q3 = Debit market needs
Q4 = Discharge of household needs.
Zonation Scenario. At present, many residents around the river use water sourced from PDAM Central Aceh Regency, so it is necessary to assume the use of facilities that still use Peusangan river water. Facilities that use water sourced from the river consist of; schools, houses of worship, markets and households, but zoning scenarios are only used in school facilities and houses of worship. The zoning scenario is divided into three, namely at a distance of .

Reasearch Methodologies

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Research Location. Research on the Analysis of the Effect of Debit needs on Hydroelectric Power Plants (PLTA) the author conducts direct observation and data collection at PT PLN (Persero) which is directly in charge as the owner of the PLTA on the Peusangan river, Central Aceh Regency. This river has a depth of . The depth of water that must be maintained is at so that the elevation that must be maintained in the river is at . Meanwhile, the depth of the fish cages is at 3.5 m from the maximum surface of the river water. The chosen Zoning distances of and 2 km where based on their proximity to key facilities using river water, such as
school and house of worship. These distance also reflect common zoning practices in Hydropower planning to balance operational needs and communuty water usage. The selection of zoning distance , and 2 km reflects standard hydropower planning practices, ensuring a balance between plant operations and community water need. Potential biases, such as reliance on institutional data, are acknowledged, and future studies are encouraged to include independent surveys for greater accuracy.

Stages of Research.

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Data Analysis. The data analysis carried out is to calculate the MHP meters with predetermined equations, then calculate the water needs, finally do the discharge and zonation scenarios.

Review

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Water Power. From the existing equation, the result is Watts from turbine 1 while in turbine 2 the result is Watts.

Turbine Exit Power. Based on the data available from PLN, the output power of turbine 1 is while for turbine 2 the result is .

Turbine Efficiency. The results obtained from the calculation of turbine efficiency 1 amounted to while turbine 2 obtained a result of .

Domestic Water Needs. Domestic water needs in the Peusangan river environment is 994,942 1/ person / day.

Table 1 Non-domestic water Needs at 500 m zonation

No | Type of Facility | Amount of Facilities | Discharge | Standard (l) | Water Needs
|  |  |  |  | (l) | (m³/s)
1 | School | 45 | 10 | 198.99 | 89544.79 | 89.54
2 | House of Worship | 57 | 2 | 198.99 | 22684.68 | 22.68
3 | Market | 1 | 1 | 198.99 | 198.99 | 0.20
4 | Total | 112428.46 | 112.43

Table 2 Non-domestic water needs at 1 km zonation

No | Type of Facility | Amount of Facilities | Discharge | Standard (l) | Water Needs
|  |  |  |  | (l) | (m³/s)
1 | School | 55 | 10 | 198.99 | 109443.64 | 109.44
2 | House of Worship | 70 | 2 | 198.99 | 27858.38 | 27.86
3 | Market | 1 | 1 | 198.99 | 198.99 | 0.20
4 | Total | 137501.00 | 137.50

Table 2 Non-domestic water needs at 2 km zonation

No | Type of Facility | Amount of Facilities | Discharge | Standard (l) | Water Needs
|  |  |  |  | (l) | (m³/s)
1 | School | 55 | 10 | 198.99 | 109443.64 | 109.44
2 | House of Worship | 70 | 2 | 198.99 | 27858.38 | 27.86
3 | Market | 1 | 1 | 198.99 | 198.99 | 0.20
4 | Total | 137501.00 | 137.50

Discharge Scenario. Analysis of release regulating weir based on cross section with average flow velocity data of obtained minimum and maximum discharge in Peusangan river. Calculation of the minimum discharge scenario obtained a result of . As for the maximum discharge, the result is .

Zonation Scenario.

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Table 4 Discharge needs at 500 m zonation (buffer zone)

No | Type of Facility | Amount of Facilities | Discharge | Standard (l) | Water Needs
|  |  |  |  | (l) | (m³/s)
1 | School | 55 | 10 | 198.99 | 109443.64 | 109.44
2 | House of Worship | 70 | 2 | 198.99 | 27858.38 | 27.86
3 | Market | 1 | 1 | 198.99 | 198.99 | 0.20
4 | Total | 137501.00 | 137.50

Table 5 Discharge needs at 1 km zonation (buffer zone)

No | Type of Facility | Amount of Facilities | Discharge | Standard (l) | Water Needs
|  |  |  |  | (l) | (m³/s)
1 | School | 55 | 10 | 198.99 | 109443.64 | 109.44
2 | House of Worship | 68 | 2 | 198.99 | 27062.43 | 27.06
3 | Market | 1 | 1 | 198.99 | 198.99 | 0.20
4 | Household |  |  |  | 994.94 | 0.99
5 | Total |  |  |  | 136705.05 | 137.70

Table 6 Discharge needs at 2 km zonation (buffer zone)

No | Type of Facility | Amount of Facilities | Discharge | Standard (l) | Water Needs
|  |  |  |  | (l) | (m³/s)
1 | School | 55 | 10 | 198.99 | 109443.64 | 109.44
2 | House of Worship | 70 | 2 | 198.99 | 27858.38 | 27.86
3 | Market | 1 | 1 | 198.99 | 198.99 | 0.20
4 | Household |  |  |  | 994.94 | 0.99
5 | Total |  |  |  | 137501.00 | 137.50

Discussion. The higher discharge requirements in the 1 km and 2 km zones indicate that these areas have a more significant impact on the operational stability of the Peusangan Hydropower Plant. Given the variation in dischare requirements, proper management is neede to maintain the plant's operational stability and prevent potential envuromental impact. The finding suggest that zones closer to hydropower plant (such as the 500 zone) have lower discharge requirements but m ,ay have a more significant impact on local ecosystem if not managed properly. Additional, zones futher away (such as the 1 km and 2 km zones) require higher discharge to meet the community's needs ensure the hydropower plant's operations. These zones should be carefully monitored and managed to avoid distruping the ecological balance.

Picture 1 Zonation Skenario

Figure 1. Picture 1 Zonation Skenario

Summary

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Based on the discussion of the analysis of the effect of discharge requirements on Peusangan Hydroelectric Power Plant, there are several conclusions, namely:

The discharge requirements for the four sectors around the river are divided into 3, in the 500 m zoning the required discharge is , in the 1 km zoning the required discharge is 137.70 , and in the 2 km zoning the required discharge is .

The minimum discharge requirement for Peusangan Hydroelectric Power Plant at 500 m zoning is while the maximum discharge is , the minimum discharge for Peusangan Hydroelectric Power Plant at 1 km zoning is , while the maximum discharge is 463.50 , and for the minimum discharge at 2 km zoning is while the maximum discharge is . Thus, the discharge needed to rotate the existing turbines at Peusangan Hydroelectric Power Plant can be fulfilled.

The results indicate that discharge requirements increase with distance from the hydropower plant, with zones at 2 km needing significantly higher discharge rates. These findings align with previous studies, highlighting the need for efficient discharge management to prevent disruptions to plant operations and local ecosystems.

Suggestions

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After conducting research on the analysis of discharge requirements at Peusangan Hydroelectric Power Plant, the following suggestions can be given:

For further research, it is hoped that it can analyze more completely about hydropower such as disturbances, loads and productivity in hydropower.

Turbines in PLTA require more attention and maintenance, because the efficiency value does not meet the existing standards, which are .

Can be input material for related agencies regarding turbine efficiency at Peusangan Hydropower Plant.

Implement advance turbine technologies to enchance efficiency and reduce enviromental stress.

Conduct regular impact assessment on local ecosystems to ensure sustainable operation.

References

The original version of this paper is available on https://www.scientific.net/EI.14.53.pdf

Azis, F., & Fitri, S. N 2020. Design of Micro Hydro Power Plant Trainer. (Vol. 1 No. 2 August 2020) (ISSN: 2723-4193)

Rizaldi. Ferri Rifki. Suhartanto, Ery & Harisuseno, Dony 2014. Reliable Discharge Analysis and Storage Simulation for Pumpe Storage Hydropower Development at Tulungan Selatan Sluice Gate.

Hariadi, H., Muchlisinalahuddin, M., & Meilisa, M. 2021. Comparative Analysis of Plta Batang Agam Turbine Performance Against Conditions at the Time of Commissioning. (Vol. 4 No. 2 June 2021) (ISSN: 2599-2018).

Pangesti, F. S. P., & Ariesmayana, A 2022. Review of Analysis of Calculation of Clean Water and Wastewater Requirements for Planning Wastewater Distribution Systems in Bumi Ciruas Permai 1 Housing in Serang Regency. (Vol. 4 No. 1 February 2022)

Noviana, S., Arsianty, D., & Normelani, E. Utilization of Tamban Canal River Water for Community Clean Water Needs in Tamban District, Barito Kuala Regency.

This ePub file was created using machine-generated methods and automated software to convert the source text into an ePub format and may contain formatting inconsistencies from the original version. Formatting may vary depending on the device used.

The original version of this eBook is available on https://www.scientific.net/book/engineering-innovations-vol-14/978-3-0364-1777-6

Engineering Innovations, ISSN: 2813-1002, Vol. 14, pp 61-70

doi: 10.4028/p-0Rvr5v

© 2025 The Author(s). Published by Trans Tech Publications Ltd, Switzerland.

Submitted:
2024-01-23

Revised:
2024-03-25

Accepted:
2024-04-03

Online:
2025-04-04

IoT Based Environmental and Water Monitoring System for Sterilization Units in Honduras

The original version of this paper is available on https://www.scientific.net/EI.14.61.pdf

Carlos Matute Molina , Fernando Calderón Perdomo , and Fernanda Cáceres Lagos

Facultad de Ingeniería, Universidad Tecnológica Centroamericana UNITEC, Tegucigalpa, Honduras

cmatute2296@unitec.edu, fernando.josue@unitec.edu, fernanda_Icl@unitec.edu

Keywords: IoT, Hospitals, Monitoring, Sterilization, Water Quality

Abstract

Sterilization is a crucial process in healthcare. The environment in which it is performed plays a critical role and any change in temperature and humidity of the environment can compromise the process. In addition, the water quality is also fundamental since its contamination and hardness can cause high impact damage to surgical material and medical equipment. It is difficult for public hospitals in Honduras to control these processes, environments, and water, possibly due to factors such as limited budgets or lack of knowledge of international guidelines. It is proposed to develop a monitoring system capable of measuring environmental and water quality parameters through the implementation of IoT for sterilization units. A vast literature review and the support provided by experts in the field allowed to establish the success criteria for the development of the prototype. The V methodology was used for the mechatronic design to obtain a complete product. By means of experimentation and finally field tests were carried out to determine the components that would integrate the system and the validation of their measurements. A monitoring system was developed to measure relative humidity, temperature, dust particles and total dissolved solids in the water. These measurements were displayed on a web based Ubidots dashboard for data visualization, storage, and export. Once the measurements were obtained, a comparison with international standards was carried out to demonstrate the importance of environmental monitoring in this area.

Introduction

The original version of this paper is available on https://www.scientific.net/EI.14.61.pdf

Sterilization plays a fundamental role in the field of healthcare, as it involves the elimination of pathogens and other microorganisms present in equipment and materials used in surgical procedures. Therefore, it is vitally important to ensure that the environment where sterilization is performed is highly safe and controlled. [1].

The environment in which the sterilization process is carried out is a critical factor that affects its effectiveness. Any change in the environment can impair the quality of sterilization. [2]. In addition, the quality of the water used in the sterilization process is also essential, since its contamination can cause major damage to the material and equipment. [3]. The Internet of Things (IoT) provides an effective solution to monitor these parameters, as it allows the collection of data in real time and the immediate identification of any deviation from normal values. [4].

The Honduran health system has several deficiencies, this could be reflected in the effectiveness of the sterilization process of surgical materials in a Sterilization and Equipment Center (SEC). Public hospitals find it difficult to control processes, controlled environments and water quality, possibly due to factors such as limited budgets or lack of technical knowledge.[5].

Environmental parameters, such as temperature, humidity and the presence of dust particles in the air, are crucial elements that should be monitored at all times in order to have an essential environment that does not compromise the sterility of the materials.[6]. Water quality is another crucial factor to consider because it also affects the lifespan of materials and equipment. [7].

During visits to a public hospital in the country, it was observed that there is a lack of adequate separation between the different areas. This situation generates unfavorable environmental conditions and facilitates the free movement of dust particles, which increases the risk of material contamination
in these areas. [8]. It is important to take measures to avoid this contamination and guarantee a safe environment free of harmful particles in the SEC. [9].

This research aims to improve the verification of environmental parameters and water quality in a sterilization plant through the implementation of a monitoring system. The objective of this paper is to develop a monitoring system capable of performing the measurement of environmental and water quality parameters by implementing IoT for this area in a public hospital in Honduras.

In the following sections the state of the art and the methodology used in the research will be discussed. In addition, the part of results and analysis will be addressed, where the results obtained in the validations, the elaborated tests and final tests will be seen. Likewise, the IoT connection and the results obtained from the platform measurements will be explained. Finally, the conclusions and thanks to the people involved in the project will be presented.

State of the Art

The original version of this paper is available on https://www.scientific.net/EI.14.61.pdf

Environmental quality monitor in an SEC: Evidence from a cancer center. In India, the concept is used that quality is related to the degree of excellence of a product. In addition, a large amount of time is required to develop, implement and monitor processes for quality control. This reduces unnecessary costs and increases the useful life of instruments and equipment. Therefore, a monitoring system was developed that would analyze four environmental parameters such as water quality, air quality, carbolization and sterility tests [10].

This system may be inefficient, but it is what some hospitals manage. The others choose to hire external companies to carry out a study of the different variables that they want to study. The cost of a study like this can be high and will depend on the area where it is intended to be carried out.

Low-cost in-hospital environmental monitoring system. In Mexico, a study was carried out on environmental factors that could influence the quality of patient care. To this end, we sought to implement a monitor of environmental parameters in medical unit facilities. Which would allow the real-time measurement of five values: temperature, relative humidity, light intensity, sound intensity and carbon monoxide concentration. [11]. Various sensors were used to measure environmental parameters, which were strategically positioned. For data capture and transmission, a PIC16F877 microcontroller was used, which is considered low cost in all areas.

IoT-based environmental monitoring system for intensive care unit in Honduras. In Honduras, not all hospital institutions have the ability to integrate a monitoring system for environmental parameters in their facilities, therefore, this must be done manually by the staff or it is something that some institutions are completely unaware of.

In Honduras, not all hospital institutions have the ability to integrate a monitoring system for environmental parameters in their facilities, therefore, this must be done manually by the staff or it is something that some institutions are completely unaware of [12]. Operating rooms are classified as critical areas due to the fact that invasive procedures are performed on patients, therefore, they are exposed or vulnerable to microorganisms entering the body, causing what is known as a nosocomial disease. Thus, due importance and attention must be given to the environment within the facilities.

To carry out the monitoring, the environments within the different areas of the hospital were studied, reaching the conclusion that the focus area would be the Intensive Care Unit (ICU). The information collected would then be transmitted through the Internet of Things for the study of the final results, obtaining the desired guide for the applicability of the system. In this way, this project demonstrates how crucial it is to have a monitoring system that validates the environmental conditions for an area as critical as the ICU. Concluding that the introduction of IoT and the ability to fully exploit a network of sensors would achieve favorable results for monitoring within complex environments. [12].

Regulations

The original version of this paper is available on https://www.scientific.net/EI.14.61.pdf

The following section presents the regulations considered for the design of the prototype developed for the research.
AAMI ST 79: 2017. This association created "The Comprehensive Guide for Steam Sterilization and Sterility Assurance in Health Care Facilities (ST79)", which provided guidance on recommended temperature and humidity levels for different areas of the sterile processing department. In 2017, due to disagreements by SEC staff, regarding the difficulty in achieving the recommended levels, the environmental parameters for each department now follow a version of ASHRAE 170.[13]. This regulation has served as a basis for the development of ideal standards depending on the conditions of the hospital.
AAMI TIR34:2014. Establishes that the water used in the cleaning, disinfection and sterilization processes of medical devices must meet certain quality criteria and recommends the use of high quality purified or demineralized water. It is recommended to regularly monitor the quality of the water used to prevent contamination of medical devices. In addition, different parameters must be taken into account to ensure water quality. [7].
ASHRAE 170-2017. Standard that establishes the minimum requirements for the design, construction, and operation of ventilation and filtration systems in health care facilities. It is widely used for the conditions that must be met within a SEC [14]. The standard's requirements for sterilization units include: air change rate per hour, indoor air quality, location and size of air intakes and discharges and differential pressure between processing areas and adjacent areas.
ISO 14664. This standard refers to a series of international standards that establish requirements and guidelines for the classification, monitoring and control of air quality in controlled environments. For example, clean rooms or areas and controlled atmosphere zones. [15]. It establishes an air cleanliness classification based on the maximum number of airborne particles allowed in a given particle size in a controlled environment. It also provides guidelines for continuous monitoring and control of air quality.

Methodology

The original version of this paper is available on https://www.scientific.net/EI.14.61.pdf

After establishing the main idea of the problem to study, we proceeded to conduct a literature review for the collection of information and the analysis of the system requirements, which allowed us to define the feasibility of the project. In addition, to make sure that it was possible to develop it in a public hospital, a field study was carried out with several visits in which the origin of some problems were observed.

The investigation continued by interviewing personnel to learn more about the infrastructure and processes carried out at the site. In this way, a complete picture of the design was obtained and compliance with the corresponding regulations or quality standards was understood. The mission was to collect as much information as possible in order to determine the implementation of the system at the plant. This led to the conclusion that two prototypes should be implemented, one to measure environmental parameters inside the clean area and the other outside the CEYE to measure water quality, which came from a well adjacent to the plant.

In addition, a system analysis was carried out to review the different sensors that could be used in the project. With the related research obtained in the elaboration of the state of the art, it was possible to determine a feasible way to develop the project, taking into account the experience of previous researchers. It was determined that the parameters to be measured would be temperature, relative humidity, air quality (concentration of dust particles) and water quality (hardness). At the conclusion of the literature review, the modules (sensors and microcontrollers) to be used were selected to start testing. Codes were programmed in Arduino IDE for each of the modules and tested individually to avoid problems at the time of integration of the entire system. Having all the corresponding materials and the appropriate research to build the system, individual tests were to be performed with them.

Subsequently, experiments were carried out with the sensors in different locations, for example, with the TDS sensor, the transducer was immersed in water brought from the hospital and then in drinking water, comparing the values with those of a conductimeter. For the temperature and humidity sensor, the industrial engineering laboratory booth was used, which had an instrument capable of measuring the corresponding parameters. In the case of the dust particle sensor, there was no feasible way to check if its values were correct, since only the manufacturer's reference was available, which was obtained through the technical data sheet. Subsequently, we proceeded to integrate the corresponding sensors with each ESP32, in one the TDS sensor for water quality was installed. The other microcontroller was mounted with the dust particle sensor and the HTU21D, each prototype with its corresponding LCD display.

The system had to have a robust and reliable aesthetic, so a bakelite PCB was adapted for system integration to improve communication and census. Then, the whole system was tested to verify its accuracy. Once the system was integrated, the values measured by the sensors were checked again using the controlled environment cabin of the industrial engineering laboratory.

Once the final system tests were completed, the communication protocol that would be used to connect the system to a cloud platform was programmed. Then, the housing parts were designed in Solidworks and exported to DFX format to be used in RDWorks. Then, we proceeded to cut the parts with a Corvus laser cutter. Subsequently, the parts were fixed together with glue and support units were installed at the base of the housing to prevent the cables from touching the base of the housing. Then, validation tests were performed with the previously mentioned sensors. With all this verified, we proceeded to perform the tests in the public hospital. At the hospital, the corresponding measurements were performed at the locations mentioned at the beginning. Data was collected for approximately one hour and stored in the cloud using Ubidots, which is a platform known for its implementation with IoT systems.

Results and Discussion

The original version of this paper is available on https://www.scientific.net/EI.14.61.pdf

For the development of the system, the monitoring of four factors considered crucial to guarantee the effectiveness of the sterilization process was established: ambient temperature, relative humidity, presence of dust particles in the air (these in one prototype) and the level of dissolved solids to determine the quality of the water (in a second prototype). The integration of components such as microcontrollers and sensors available in the national market was fundamental for the design of the prototype.

It was possible to design a system integrated by two prototypes with different electronic components. An environmental prototype had: HTU21D temperature and relative humidity sensor, SDS011 dust sensor, ESP32 microcontroller and 20x4 LCD screen. On the other hand, the water quality prototype had: TDS sensor, ESP32 microcontroller and LCD screen.

In this way, we proceeded to establish the connection with the IoT platform, in the case of the prototype for monitoring environmental parameters. The dashboard is shown in Fig. 1. Thus achieving the implementation of this technology in the facilities of a sterilization unit, an area of vital importance for any hospital. The system underwent a field test lasting one hour and thirty minutes. The system was subjected to a field test lasting one hour and thirty minutes as shown in Fig. 2.

During this test, revealing information was obtained indicating that the conditions under which this procedure is carried out are not suitable. High temperature values, acceptable levels of relative humidity and sharp fluctuations in dust particles were observed. (See Fig. 3, Fig. 4 and Fig. 5).

Regarding humidity, it was constantly maintained in a range of . These values could be considered acceptable according to the regulations established by AAMI. It is also relevant to take into account that this area of the plant lacks an environmental conditioning system.

However, the temperature values turned out to be in a high range when considering what is established by international standards. The readings were maintained in a range of for the reason

Fig. 1: Dashboard in Ubidots platform.

Fig. 1. Dashboard in Ubidots platform.

Fig. 2: Area where the packaging of washed material takes place.

Fig. 2. Area where the packaging of washed material takes place.

Fig. 3: Relative Humidity Graph in IoT platform from 10:54am to 12:29pm

Fig. 3. Relative Humidity Graph in IoT platform from 10:54am to 12:29pm

Fig. 4: Ambient Temperature Measurement Graph in IoT platform from 10:54am to 12:29pm

Fig. 4. Ambient Temperature Measurement Graph in IoT platform from 10:54am to 12:29pm

Fig. 5: PM 10 (top) and PM 2.5 (bottom) particulate matter measurement graphs in IoT platform.

Fig. 5. PM 10 (top) and PM 2.5 (bottom) particulate matter measurement graphs in IoT platform.

mentioned above. This indicates that the environment contains conditions that favor the proliferation of microorganisms that benefit from high temperatures.

The most significant results were obtained from the suspended particulate measurements. Noticeable changes in PM 2.5 and PM 10 levels were observed during the morning hours (8:00 AM to 12:00 PM), matching with a greater presence of personnel. These changes were evident at specific times, such as during the transfer of washed material for packaging.

Having mentioned the above, this research proposes the following system which has been developed to better control the variables to be monitored in a sterilization plant. Fig. 6 shows the connections used to build the system.

Fig. 7 shows a block diagram of the environmental parameters prototype. The microcontroller is powered with a 5 V voltage, and thus provides 3.3 V to power the SDS011 (S1) and HTU21D (S2) sensors. These sensors collect information on ambient temperature, relative humidity and the concentration of PM2.5 and PM10 particles. Once they perform the measurements, they send the data to the microcontroller. The latter is responsible for processing the information obtained and then sending this data to be displayed both on an LCD screen and on the Ubidots platform.

Fig. 8 shows the block diagram of the water quality monitoring prototype. The microcontroller is powered with a voltage of 5 V , and provides 3.3 V to power the TDS sensor . When the TDS sensor performs measurements, it sends the collected information about the total dissolved solids (R(s)). The ESP32 then processes this data and sends it for display on the LCD.

The shortcomings related to the coding and quality of the TDS sensor model meant that it was not possible to establish an effective connection with the IoT platform. This made it impossible to obtain data through the sensor, so it was decided to perform manual monitoring.

To evaluate the accuracy of the sensor, tests were used with distilled water as a reference, since it is considered "pure water" due to its lack of electrical conductivity. This is because distilled water does not contain charged ions, such as dissolved minerals, which are normally responsible for conducting electrical current through water.

This was performed for a 10 -minute interval with a sample of the water used in the hospital sterilizers. In this case, a field test was not possible due to the installation of new equipment in the sterilization

Fig. 6: Diagram of the IoT connection of the environmental parameters prototype.

Fig. 6. Diagram of the IoT connection of the environmental parameters prototype.

Fig. 7: Block diagram of the environmental prototype

Fig. 7. Block diagram of the environmental prototype

Fig. 8: Block diagram of the water prototype

Fig. 8. Block diagram of the water prototype

Fig. 9: Measurements of the TDS sensor for water quality monitoring in the IoT platform.

Fig. 9. Measurements of the TDS sensor for water quality monitoring in the IoT platform.

Fig. 10: Final housing design in SolidWorks

Fig. 10. Final housing design in SolidWorks

area and the integration of the reverse osmosis system. Nevertheless, the test was carried out and its operation was effectively verified for an extended period of time. Obtaining the values shown in Fig. 9. The sensor data were compared with a calibrated Thermo Orion model 145 conductivity meter. The data between them showed a significant difference indicating that the sensor is not accurate.

It is recommended to use the Gravity TDS sensor, since the supplier provides a library compatible with DFROBOT that facilitates the use of the sensor. The sensor used in this project was of the Keyestudio brand, with which there were connection problems, because when connected to the server, the sensor stopped working, providing very different data to the conductivity meter. In addition, with the Gravity V2 sensor it is possible to measure TDS and electroconductivity simultaneously.

In order to integrate the two devices, the decision was made to design a housing using SolidWorks software. Acrylic, a material recognized for its durability and transparency, was implemented. To ensure the accuracy of the cuts, a laser cutting process was employed, which ensured high quality results and perfect fit in each component of the housing. This design choice and manufacturing method enabled the creation of a rugged case, providing adequate protection for the devices in question. (See Fig. 10). Both prototypes are shown in the Fig. 11.

Conclusions

The original version of this paper is available on https://www.scientific.net/EI.14.61.pdf

An IoT monitoring system was developed to measure environmental parameters in a green area of a SEC. The TDS sensor could not be integrated into Ubidots due to coding issues and lack of compatibility with recommended protocols. The TDS sensor was used only for measurements and display on screen.

Fig. 11: Final prototypes

Fig. 11. Final prototypes

Two strategic points were found in the SEC for prototyping. The first was in the packaging area due to temperature, humidity and dust disturbances. The second point was the cistern where water is supplied for the autoclaves. However, the TDS sensor only works properly when the tank is almost full, as the measurements will be 0 otherwise.

The findings in airborne particulate measurements have generated particular interest due to the abrupt variations observed in particulate concentrations. These results indicate the possibility that the wrappings used for sterilization may not meet medical grade standards or may have lost their ability to act as a barrier against the entry of microorganisms. It is important to note that this type of packaging should have a controlled porosity of no more than 0.5 microns. This may open an opportunity to continue this study with further field tests in hospitals in the country.

It has been demonstrated that this type of system is efficient in different areas of the hospital such as operating rooms, intensive care units, isolation rooms, among others. All of them have in common that they are critical areas in a hospital and that they must have a controlled environment to ensure a safe environment for patients and health personnel. With a monitoring system, preventive or corrective measures can be taken quickly to help maintain an optimal environment that does not affect the integrity of patients or other people. IoT systems are of great benefit to hospitals, as they can help to efficiently manage resources and collaborate with the care of some equipment.

References

The original version of this paper is available on https://www.scientific.net/EI.14.61.pdf

FGI, Guidelines for design and construction of hospitals, 2018th ed. St. Louis, MO: Facility Guidelines Institute, 2018.

A. Neville, "Pautas Sobre Esterilización - Tercera Edición 2020," Mar. 2021. [Online]. Available:

T. A. Kremer, "Water Quality in Medical Device Processing: The Power of Prevention," Biomedical Instrumentation and Technology, Jun. 2020.

A. Gerodimos, L. Maglaras, M. A. Ferrag, N. Ayres, and I. Kantzavelou, "IoT: Communication protocols and security threats," Internet of Things and Cyber-Physical Systems, vol. 3, pp. 1-13, 2023. [Online]. Available:

C. Q. D. M. Bruna and K. U. Graziano, "Temperatura e umidade no armazenamento de materiais autoclavados: revisão integrativa," Revista da Escola de Enfermagem da USP, vol. 46, no. 5, pp. 1215-1220, Oct. 2012. [Online]. Available: sci_arttext&pid=S0080-62342012000500025&1ng=pt&tlng=pt

OMS, "AMR-Manual de Esterilizacion para Centros de Salud," 2008.

AAMI, "AAMI TIR34 2014 R 2017 Water Reprocessing of medical devices," 2017. [Online]. Available:

T. Dropson, "Medidor TDS: ¿Qué es y qué mide realmente? | Dropson," Dec. 2019. [Online]. Available: medidor-tds-que-es-y-que-mide-realmente/

I. S. Kurniawansyah, M. Abdassah, and S. Gondodiputro, "Relationship between Temperature and Humidity on Sterility of Reusable Instruments in Hospital's CSSD," International Journal of Pharmaceutical Sciences Review and Research, no. 45, Jun. 2015.

D. Basu, A. Das, and J. D. Rozario, "A brief discussion on environmental quality monitoring required in a central sterile supply department: Evidence from a cancer center in eastern India," Infection Control & Hospital Epidemiology, vol. 41, no. 5, pp. 624-625, May 2020, publisher: Cambridge University Press.

G. A. M. Chavez, "Desarrollo de un sistema de monitoreo ambiental intrahospitalario de bajo costo basado en una computadora personal." Memorias del XLI Congreso Nacional de Ingeniería Biomédica, vol. 5, no. 1, 2018. [Online]. Available: . php/memorias/article/view/703

S. A. S. Girón, B. A. C. Morales, F. de Lourdes Cáceres Lagos, and F. E. N. Rodríguez, "Environmental Monitoring System with Wireless Sensing and IoT Technologies for Intensive Care Units," in 2023 8th International Conference on Control and Robotics Engineering (ICCRE), Apr. 2023, pp. 150-155, iSSN: 2835-3722.

C. Technology, "Fine tuning HVAC levels," Sep. 2018. [Online]. Available: .

ASHRAE, "ASHRAE Standard 170_2017," 2017. [Online]. Available:

ISO, "ISO 14644-1 Version 2015," 2015. [Online]. Available:

This ePub file was created using machine-generated methods and automated software to convert the source text into an ePub format and may contain formatting inconsistencies from the original version. Formatting may vary depending on the device used.

The original version of this eBook is available on https://www.scientific.net/book/engineering-innovations-vol-14/978-3-0364-1777-6

Engineering Innovations, ISSN: 2813-1002, Vol. 14, pp 71-84

doi: 10.4028/p-a2fF1P

© 2025 The Author(s). Published by Trans Tech Publications Ltd, Switzerland.

Submitted:
2024-03-22

Revised:
2024-08-02

Accepted:
2024-09-16

Online:
2025-04-04

Biomimetic Robot with Peristaltic Locomotion Integrating 3D Printing and IoT Technologies

The original version of this paper is available on https://www.scientific.net/EI.14.71.pdf

José L. Sandoval-Ferrufino , Bryan E. Alvarado-Orellana , and Julio Valentin Fajardo

Universidad Tecnológica Centroamerica (UNITEC), San Pedro Sula Honduras

Universidad Galileo, Guatemal, Guatemala

jose_sandoval@unitec.edu, orellanab639@unitec.edu, julio.fajardo@galileo.edu

Keywords: Robotics, Biomimetic Robots, Peristaltic locomotion, 3D Printing, Internet of Things (IoT), Real-Time Data Collection

Abstract

This paper presents the design and development of a biomimetic robot utilizing peristaltic locomotion as a method of movement. Inspired by nature, the robot addresses key limitations encountered in conventional robotic systems, such as imbalance affecting the center of mass and restricted adaptability to complex terrains. Integration of Internet of Things (IoT) technology enables real-time data collection, contributing to enhanced performance and efficiency across diverse environments. The utilization of advanced 3D printing techniques facilitates rapid prototyping and customization of robot components. Experimental evaluation demonstrates the effectiveness of the biomimetic approach, highlighting its potential for applications in exploration, disaster response, and healthcare. Analysis of results informs future research directions, focusing on further optimization and integration of emerging technologies to advance robotic capabilities.

Introduction

The original version of this paper is available on https://www.scientific.net/EI.14.71.pdf

Peristaltic locomotion has been an area of interest in nature-inspired robotics. It is based on the movement of wave-like contractions and expansions that mimic the locomotion of certain biological species, such as snakes and worms. This method of movement has proven to be particularly effective in complex and hard-to-reach environments, where conventional locomotion methods may be inefficient or insufficient. [1] Peristaltic locomotion has been studied in a variety of contexts, including medical applications and space exploration. In medicine, its potential for the development of miniaturized medical devices that can navigate through the human body with precision and control has been explored [2]. It has also been considered as a possible movement mechanism in space exploration for robots intended to explore challenging terrains on other planets or moons [3].

An important challenge in modern robotics lies in the ability to navigate efficiently through complex environments. The development of a peristaltic robot seeks to address this challenge by leveraging its unique movement capabilities, particularly suited for exploration in irregular and challenging terrains. Peristaltic locomotion, inspired by biological organisms such as snakes and worms, involves rhythmic contractions and expansions to propel the robot forward, enabling it to traverse uneven surfaces, tight spaces, and obstacles with relative ease[4]. This mode of locomotion offers distinct advantages over traditional wheeled or legged robots, as it allows for smooth adaptation to varying terrain conditions and minimizes the risk of getting stuck or overturned. Additionally, peristaltic robots exhibit a lower ground pressure, reducing the likelihood of causing damage to delicate environments or structures during exploration. By addressing the limitations of conventional locomotion methods, peristaltic robots hold great promise for applications in fields such as search and rescue, environmental monitoring, and planetary exploration, where navigating challenging terrains is essential for mission success[5]. Furthermore, advancements in materials science, sensor technology, and control algorithms are poised to further enhance the capabilities and versatility of peristaltic robots, paving the way for innovative solutions to complex challenges in robotics and beyond.

The development of robotic locomotion faces several challenges. Firstly, imbalance due to the motor's position affecting the center of mass can lead to undesired slipping during robot movement[6]. This occurs when friction forces between the followers and the surface are insufficient to counterbalance the motor's unbalanced weight, resulting in uncontrolled and less efficient movement. Moreover, center of mass imbalance can influence the robot's direction of movement. On inclined or uneven surfaces, slipping caused by imbalance can cause the robot to deviate from a straight path, affecting its ability to follow a desired trajectory. Secondly, traditional robots lack IoT integration, limiting their ability to collect and utilize real-time data for performance optimization and environmental adaptation. Lastly, conventional robots are not teleoperated, meaning they lack remote control capabilities, which can restrict their usability and flexibility in dynamic environments. Addressing these limitations is crucial for advancing the capabilities and applicability of robotic systems in various domains.

Our primary objective is to design and develop a biomimetic robot capable of peristaltic locomotion, addressing key limitations present in conventional robotic systems. By harnessing the principles of peristaltic movement inspired by nature, we aim to enhance the robot's adaptability and maneuverability in complex and challenging environments. Additionally, we seek to integrate Internet of Things (IoT) technology into the robot's design, enabling real-time data collection and analysis to improve its performance and efficiency across various applications. Our motivation stems from the recognition of the potential of biomimetic robotics and IoT integration to revolutionize robotic capabilities and address critical challenges in fields such as exploration, disaster response, and healthcare. Through this project, we aspire to contribute to the advancement of robotics by pushing the boundaries of innovation and providing practical solutions to real-world problems.

In the subsequent sections, we detail the materials employed and the methodologies followed throughout the project, encompassing the design, fabrication, and testing phases. We delve into the selection of components, including motors, sensors, and structural materials, as well as the utilization of advanced 3D printing techniques for rapid prototyping and customization. Additionally, we elucidate the experimental setup and procedures conducted to evaluate the performance of the biomimetic robot in various simulated and real-world scenarios, highlighting key findings and observations derived from the collected data. Through meticulous analysis, we elucidate the implications of our results, shedding light on the efficacy and limitations of the proposed approach. Furthermore, we present our conclusions, synthesizing the key insights gained from the study and outlining avenues for future research and development. Emphasis is placed on the potential for further optimization and refinement of the robot's design and functionality, as well as the exploration of novel applications and integration of emerging technologies to enhance its capabilities.

State of Art

The original version of this paper is available on https://www.scientific.net/EI.14.71.pdf

Locomotion. The peristaltic locomotion of worm robots was optimized using Bézier curves, enhancing smoothness and flexibility in route planning [1]. The efficiency of peristaltic wave planning was significantly increased with the introduction of an elliptical trajectory algorithm [2] [5]. A hybrid controller ensured gait cycle stability in bio-inspired robots, considering angular constraints, and utilizing an extended state observer [3] [7]. They promise more adaptable and efficient worm robots for various applications. In the study on undulatory locomotion, a minimally actuated robot is presented that efficiently moves across highly flexible surfaces, surpassing worm locomotion [8]. Another approach highlights the development of a bioinspired soft pneumatic robot resembling segmented worms, utilizing elastomers and casting techniques to achieve efficient peristaltic locomotion [5]. In an innovative proposal, a soft crawling robot with multiple modes of movement and high environmental adaptability is introduced, employing pneumatic actuators and selective laser sintering technology [9]. The efficiency, adaptability, and versatility of soft robots in various applications.

Another example is a deformable mobile robot was designed capable of adjusting its shape to overcome obstacles and adapt to the environment, highlighting the importance of flexibility in locomotion [10]. A study presents a crawling robot inspired by inchworms, with a compact and versatile design for
inspecting metal pipes [11]. A deep learning approach based on image processing for shape control of continuous soft robots is introduced, facilitating intuitive and precise teleoperation [12]. These innovations underscore the diversity and practical application of flexible robots in complex environments. Also, analysis of obstacle-assisted locomotion in snake robots, enhancing performance through force reaction analysis and experiments in various environments [13]. Optimization of greenhouse design for efficient navigation of mobile robots through differential evolution, considering various aspects such as required space [14]. A collective conditioned reflex mechanism for multi-robot systems, inspired by animal collective behaviors, enhancing teams' responsiveness in emergency situations [15]. These advancements contribute to the development of robotics in specific applications and improving efficiency in different contexts.

Experimental validation of the locomotion efficiency of worm-like robots and contact compliance is conducted, demonstrating clear correspondence between experimental results and theoretical predictions considering tangential compliance in the interaction between the robot and the flexible environment [16]. A Hybrid Active Disturbance Rejection Controller (H-ADRC) is presented, designed to regulate the gait cycle of a worm-inspired robotic device, achieving the execution of the proposed gait cycle despite disturbances and uncertainties in the model, surpassing the performance of PID and PD controllers [7]. Furthermore, the reaction force in obstacle-assisted locomotion of a snake robot using segmented helices is analyzed, evaluating conditions for maintaining contact and factors interfering with propulsion, and theoretically verifying these aspects with obstacle-assisted locomotion experiments [13]. These studies contribute to the development of efficient serpentine robots in complex environments and the understanding of the forces involved in their locomotion.

Structures. Inspired by the flexibility of worms, FabricWorm and MiniFabricWorm utilize textiles in soft robotics, enhancing structural smoothness and reducing rigid components [4]. A minimally invasive manipulator, with redundant kinematics, incorporates sensors for neurosurgical procedures, highlighting its flexibility and precise control [2]. A new design of soft caterpillar-like robot demonstrates efficient dual locomotion through the interaction of electricity and moisture, improving environmental adaptability [6]. Innovation in structural flexibility and advancement in soft crawling robots. Also, actuators of Twisted and Coiled Polymer (TCP) stand out in soft robotics for their powerful, hysteresis-free stroke, as seen in the soft crawling robot inspired by spines, demonstrating proven effectiveness and speed [17]. A multimodal soft robot activated by dielectric elastomer mimics a worm, featuring lightweight design and variable speeds based on electrical excitation [18]. Inspired by earthworms, a pneumatic crawler robot with suction cups on its feet demonstrates high stability and precision in direction, adapting to diverse surfaces [19]. Various advancements in soft robotics showcase versatile, effective designs tailored to different surfaces.

Taking inspiration from migratory birds, a worm robot was designed to be guided by a magnet, demonstrating environmental adaptability and magnetically guided movement [20]. A gripping module for robotic snakes was proposed to perform search and rescue tasks, with the capability to accommodate fingers, grasp objects, and include a camera [21]. A proactive neural control was suggested, allowing robots to adapt their bodies to maintain efficient contact with the ground on complex terrains [22]. The diversity of approaches aimed at enhancing mobility and functionalities of robots.

In interaction with biological environments, worm robots aim to overcome the challenges of flexibility and low friction in minimally invasive medical procedures [23]. The adaptability and flexibility of biological tissues are emphasized, challenging the design of worm robots for navigation in biological vessels, with a focus on interaction and locomotion efficiency. This study derives conditions based on flexibility, friction coefficients, and external forces [24]. Additionally, a broader analysis is presented, incorporating dynamically and statically friction coefficients, experimentally validated [16]. These studies advance the understanding of robot-environment interaction in invasive medical contexts.

Fig. 1: Model design in Solidworks.

Fig. 1. Model design in Solidworks.

A single-actuator peristaltic robot designed for subsurface geological exploration and device placement is presented, employing a worm mechanism with a cam follower configuration utilizing peristaltic displacement [6]. On the other hand, trajectory planning for worm-inspired robots akin to earthworms is addressed using Rapidly-exploring Random Trees (RRT) algorithms, highlighting the efficiency of the two-step enhanced RRT algorithm for generating peristaltic waves and facilitating motion planning in these robots [3]. Additionally, a validation method is proposed for Magneto-Inertial Measurement Units (M-IMUs) in the analysis of upper limb motion through an anthropomorphic robot, demonstrating its efficacy in evaluating the performance of these sensors in accurately tracking specific angular trajectories [25]. These advancements contribute to research in robotics applied to subsurface exploration, worm-type robot locomotion, and kinematic analysis of upper limbs.

Materials and Methods

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Methods. Designing and developing a biomimetic robot that employs peristaltic locomotion as a method of movement is the primary objective of this study. This robot is proposed as an innovative solution to overcome various limitations encountered in conventional robots, such as payload restrictions, steering difficulties, and center of mass imbalance issues. 1Additionally, the feasibility of manufacturing these robots using 3D printing will be explored, with a particular focus on cost optimization. The integration of Internet of Things (IoT) technologies will enable the robot to collect relevant data during its operation, thereby enhancing its performance and efficiency across various environments and applications. 2 The methodology involves the design and experimental validation of a 3D-printed continuum robot with integrated sensors for neurosurgery. This robot features hyperredundant kinematics and integrated sensor modules that allow real-time measurement of control variables and control of the curvature and orientation of the robot's flexible end. This study provides an insightful perspective on how sensor integration in 3D-printed robots can enhance capabilities and precision in neurosurgery applications, inspiring and complementing future project developments. [2]

Furthermore, a fascinating insight is provided into how the biology of creatures in nature can inspire the design and development of robots with similar locomotion capabilities. Specifically, the worm-inspired robot presents an innovative solution for inspecting pipelines in complex environments such as the oil and gas industry. By mimicking the morphology and behavior of a inchworm, the robot employs a compact and versatile design with a passive adhesion mechanism that allows it to effectively traverse curved metal surfaces, offering a practical and efficient solution for remote pipeline inspections [11].

Significant advancements in the field of soft robotics are presented with the development of a modular and flexible robotic snake capable of motion planning and iterative learning control. The

Fig. 2: Proposed method

Fig. 2. Proposed method

significance of this work lies in creating a robot capable of mimicking the capabilities of biological snakes, making it suitable for applications such as confined space inspection, search and rescue, and disaster response. Moreover, the incorporation of integrated curvature sensors and feedback-based control allows the robotic snake to autonomously adjust its movement, increasing its ability to navigate obstacles and complex environments more efficiently. [26]

To develop the project, a systematic approach was followed, comprising several key steps. Initially, extensive research was conducted to understand the principles of peristaltic locomotion and identify existing technologies and methodologies relevant to the project's objectives. Subsequently, a comprehensive design phase was undertaken, leveraging tools such as SolidWorks to create detailed models of the robot and its components. Drawing inspiration from existing research articles, the design was adapted and optimized to address specific challenges, such as balance and adaptability to different terrains. Once the design was finalized, the fabrication process commenced, utilizing 3D printing techniques to manufacture the custom components. Concurrently, IoT capabilities were integrated into the design, enabling real-time data collection, remote monitoring, and predictive maintenance functionalities. Following fabrication, rigorous testing and experimentation were conducted to evaluate the robot's performance across various parameters, including locomotion speed, stability, and adaptability to different surfaces. Results from these experiments informed iterative design improvements, ensuring the continual refinement and optimization of the robot's functionality. Throughout the project, interdisciplinary collaboration and a systematic, iterative approach were key to achieving the project's objectives and advancing the frontier of biomimetic robotics. Simulations and tests were pivotal in assessing the performance and reliability of the robotic system. SolidWorks served as the primary tool for conducting these simulations, allowing for virtual testing of the robot's design and functionality before physical fabrication.Through SolidWorks, various simulations were carried out to analyze factors such as structural integrity, stress distribution, and mechanical interactions within the system. These simulations provided valuable insights into potential design flaws or weaknesses, enabling preemptive adjustments to enhance the robot's robustness and efficiency. Additionally, SolidWorks facilitated dynamic simulations to evaluate the robot's locomotion patterns and stability on different terrains, aiding in the optimization of leg configurations and movement mechanisms. Subsequent physical tests validated the findings from the simulations, corroborating the effectiveness of the design modifications and providing further opportunities for refinement. Overall, the combination of virtual simulations and physical testing enabled a comprehensive evaluation of the robotic system, ensuring its readiness for real-world applications while minimizing risks and costs associated with iterative prototyping.

The development of this investigation would constitute a significant advancement in science by addressing several innovative aspects in the field of robotics. Firstly, the exploration of peristaltic locomotion represents an underdeveloped area with considerable potential for applications across various fields, ranging from space exploration to medicine. The combination of this approach with 3D printing techniques provides a platform for efficient and customized manufacturing of biomimetic robots, further expanding the possibilities for design and application. Additionally, the integration of Internet of Things (IoT) technologies for real-time data collection allows for continuous feedback and improvement of the robot's performance in a variety of environments and operational scenarios. Collectively, these elements constitute a valuable contribution to science by advancing the frontier of knowledge in robotics, offering new solutions to complex challenges in both research and practical applications of robotic technology.

Materials.

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3d Printing. Utilizing 3D printing as a cost-effective manufacturing method holds significant promise in advancing the field of robotics. This technique allows for the fabrication of complex geometries and intricate structures with high precision and efficiency, enabling the creation of customized components tailored to specific robotic designs[3]. One of the key advantages of 3D printing is its ability to reduce material waste and production costs compared to traditional manufacturing processes. By utilizing additive manufacturing techniques, where material is deposited layer by layer according to a digital model, 3D printing minimizes the need for expensive tooling and machining, making it particularly suitable for prototyping and small-scale production of robotic components. Additionally, 3D printing offers versatility in material selection, allowing for the use of a wide range of materials, including plastics, metals, and composites, to meet the specific mechanical and functional requirements of robotic systems. Moreover, the ability to rapidly iterate designs and make on-the-fly adjustments further enhances the agility and adaptability of the manufacturing process, facilitating the exploration of novel robotic concepts and accelerating the development timeline. Overall, the integration of 3D printing technologies into the fabrication of robotic systems represents a significant step forward in achieving cost-effective and customizable solutions, paving the way for innovation and advancement in the field of robotics.

Utilizing 3D printing for manufacturing offers significant cost advantages compared to traditional manufacturing methods. Unlike subtractive manufacturing processes such as milling or machining, which involve cutting away material from a solid block, 3D printing is an additive manufacturing process that builds objects layer by layer from digital designs [4]. This additive approach minimizes material waste, as only the necessary amount of material is used to create the final product, resulting in reduced material costs. Moreover, 3D printing eliminates the need for expensive tooling and setup typically associated with traditional manufacturing methods. In traditional manufacturing, specialized tooling and molds must be created for each part, leading to high upfront costs and longer lead times. In contrast, 3D printing allows for rapid prototyping and production without the need for costly tooling, enabling quicker turnaround times and lower initial investment.

Additionally, the versatility of 3D printing technology allows for the fabrication of complex geometries and intricate designs that would be difficult or impossible to achieve using traditional manufacturing methods. This design freedom not only expands the range of possible applications but also reduces the need for assembly and post-processing steps, further streamlining the manufacturing process and reducing costs. Furthermore, the accessibility of 3D printing technology has increased in recent years, with a wide range of affordable desktop 3D printers available on the market. This democratization of manufacturing enables individuals and small businesses to harness the benefits of 3D printing for rapid prototyping, custom fabrication, and small-scale production at a fraction of the cost of traditional manufacturing methods [2]. Overall, the cost-effectiveness of 3D printing makes it an attractive option for manufacturing robotic components, offering affordability, flexibility, and efficiency compared to conventional manufacturing techniques.

Utilizing 3D printing for manufacturing the robot components involved a systematic approach starting with the creation of detailed models in SolidWorks. Drawing inspiration from existing research articles, our design process involved adapting and optimizing the robotic model to address specific challenges encountered in locomotion, particularly concerning balance and adaptability to various terrains[17]. SolidWorks provided a robust platform for modeling intricate geometries and mechanical structures, enabling us to refine the design iteratively while ensuring compatibility with the manufacturing process.

The adaptation of the existing design involved significant modifications to accommodate peristaltic locomotion and overcome issues related to imbalance [19]. This included redesigning the robot's chassis and integrating counterweight mechanisms to redistribute mass effectively, thereby enhancing stability and maneuverability. Additionally, adjustments were made to ensure the robot's ability to traverse diverse surfaces with ease, necessitating the incorporation of features such as reinforced traction elements and optimized wheel configurations. Furthermore, the utilization of 3D printing technology enabled rapid prototyping and customization of components, facilitating the implementation of design iterations and fine-tuning of mechanical properties. This iterative design process allowed for the exploration of various design alternatives and optimization strategies, ultimately resulting in a robust and efficient robotic system capable of peristaltic locomotion on a variety of surfaces.

In summary, the integration of 3D printing technology in the design and manufacturing process played a crucial role in realizing the robot's capabilities. By leveraging SolidWorks for precise modeling and adaptation of existing designs, coupled with iterative prototyping enabled by 3D printing, we were able to address key challenges and create a novel robotic platform capable of efficient and adaptable locomotion.

Esp32. The ESP32 is a powerful microcontroller and system-on-chip (SoC) solution widely used in the robotics field for its versatility, performance and cost-effectiveness. Developed by Espressif Systems, the ESP32 combines a dual-core Xtensa LX6 microprocessor with integrated Wi-Fi, Bluetooth and low-power capabilities, making it an ideal choice for a wide range of robotic applications. One of the key features of the ESP32 is its integrated Wi-Fi and Bluetooth connectivity, which enables seamless communication with other devices and networks, as well as remote control and monitoring capabilities. This enables the implementation of advanced functionalities such as wireless transmission of sensor data, cloud connectivity, and mobile application integration, enhancing the overall functionality and versatility of robotic systems [27]. The ESP32 offers a comprehensive set of peripheral interfaces and integrated hardware acceleration for tasks such as encryption, hash functions, and digital signal processing (DSP), making it ideal for computationally demanding tasks in robotics. The flexibility of its GPIO (general purpose input/output) pins and support for popular communication protocols such as I2C, SPI and UART further extend its capabilities, enabling easy integration with a wide range of sensors, actuators and peripheral devices commonly used in robotics. The ESP32's low-power capabilities make it suitable for battery-powered, low-power robotic applications, enabling extended operation in remote or resource-constrained environments. Its advanced power management features, including multiple sleep modes and dynamic frequency scaling, help optimize power consumption and extend battery life, making it an attractive option for mobile and autonomous robotics. Overall, the ESP32 is a versatile and feature-rich platform that offers a compelling combination of performance, connectivity and power efficiency, making it an ideal choice for a wide range of robotics projects and applications.

Dht11. The DHT11 is a widely used sensor for measuring temperature and humidity in a variety of applications, including robotics. This sensor offers a simple and cost-effective solution for monitoring environmental conditions in real time. One of the main features of the DHT11 sensor is its simplicity of use. It consists of a calibrated digital signal output that can be easily interfaced to microcontrollers such as the ESP32. The sensor uses a capacitive humidity sensor and a thermistor to measure relative humidity and temperature, respectively [28]. The DHT11 sensor offers a wide measurement range for both temperature and humidity, typically ranging from to for temperature and 20Another
advantage of the DHT11 sensor is its low cost, which makes it an attractive option for projects with budget constraints. In addition, it requires a minimum of external components for its operation, which simplifies the design and integration process. However, it is essential to note that the DHT11 sensor has some limitations. It can exhibit relatively slow response times compared to other sensors, and its accuracy can be affected by factors such as temperature variations and external interference. Therefore, it may not be suitable for applications requiring high-speed or high-accuracy measurements. Overall, the DHT11 sensor is a practical and affordable solution for monitoring temperature and humidity in robotics projects, offering simplicity, reliability and cost-effectiveness. Its ease of use and compatibility with microcontrollers such as the ESP32 make it a popular choice for hobbyists and enthusiasts alike.

IoT. Integrating components such as the ESP32 microcontroller, DHT11 temperature and humidity sensor, and 3D-printed parts into an Internet of Things (IoT) framework allows for the creation of a versatile robotic system capable of real-time data collection, analysis, and remote monitoring as shown in Figure 3. The ESP32 facilitates wireless communication and connectivity with IoT platforms, enabling the transmission of sensor data to cloud servers for storage and processing. Through IoT-enabled applications, users can remotely control and monitor the robot's operation, receive notifications based on environmental conditions captured by the DHT11 sensor, and leverage cloud-based analytics for predictive maintenance and optimization. This seamless integration of robotics, sensor technology, and IoT infrastructure enhances the robot's functionality, efficiency, and adaptability, offering new possibilities for diverse applications in fields such as environmental monitoring, smart agriculture, and industrial automation.

The integration of Internet of Things (IoT) technologies into robotic systems offers a multitude of benefits, particularly in enhancing the functionality, efficiency, and adaptability of the robot [29]. By incorporating IoT capabilities into the robot, it becomes capable of collecting, analyzing, and communicating data in real time, enabling a range of advanced functionalities.

One of the key benefits of IoT in robotic systems is remote monitoring and control [30]. With IoT-enabled sensors and connectivity, users can remotely monitor the robot's operation, gather environmental data, and receive notifications or alerts in real-time. This capability is particularly valuable in applications such as environmental monitoring, where the robot can collect data on temperature, humidity, air quality, and more, and transmit this information to a central database or cloud platform for analysis.

Moreover, IoT facilitates predictive maintenance and optimization of robotic systems. By continuously monitoring sensor data and performance metrics, the robot can detect signs of wear, damage, or malfunctions in its components and preemptively notify users or operators of potential issues. This proactive approach to maintenance can help prevent costly downtime and equipment failures, leading to improved reliability and operational efficiency.

Furthermore, IoT enables data-driven decision-making and optimization of robotic operations. By collecting and analyzing large volumes of sensor data, the robot can identify patterns, trends, and anomalies in its environment and adjust its behavior or parameters accordingly. This adaptive capability allows the robot to optimize its performance in real-time, responding dynamically to changing conditions and requirements.

Additionally, IoT integration enhances collaboration and coordination between robotic systems and other devices or systems within the broader IoT ecosystem. By leveraging standardized communication protocols and interoperability standards, robots can seamlessly exchange data and collaborate with other IoT devices, sensors, and platforms, enabling more sophisticated and integrated applications.

In addition to the development of the robotic system, real-time temperature data collection was implemented as part of the research. 5 Through the integration of IoT technologies, temperature readings from the sensor were continuously collected and transmitted to a cloud-based platform for visualization and analysis. This real-time monitoring capability provided valuable insights into environmental

Fig. 3: IoT proposed arquitecture.

Fig. 3. IoT proposed arquitecture.

conditions and fluctuations, enabling researchers to make informed decisions and adjustments as necessary. The IoT infrastructure facilitated seamless data transmission and visualization, allowing for remote access to temperature data from any internet-enabled device. This capability proved invaluable in monitoring the robot's performance and environmental conditions during operation, facilitating timely interventions and optimizations to enhance efficiency and reliability. Overall, the integration of IoT significantly enhanced the research process by enabling real-time data collection, visualization, and analysis, demonstrating its critical role in advancing robotics research and development.

Overall, the integration of IoT technologies into robotic systems offers numerous benefits, including remote monitoring and control, predictive maintenance, data-driven optimization, and enhanced collaboration. By harnessing the power of IoT, robotic systems can become more intelligent, autonomous, and capable of addressing a wide range of applications and challenges in diverse domains.

Experiments and Results Analysis

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The robot's evaluation will be conducted through a series of rigorous tests designed to assess its performance in real-life applications. These tests will include obstacle navigation, terrain adaptability, and the efficiency of its peristaltic movement in various environments. Additionally, the robot's ability to collect and transmit data in real-time via IoT integration will be examined to ensure it can operate autonomously and respond to changing conditions. The results from these evaluations demonstrated the robot's robustness and versatility, confirming its potential for practical applications such as search and rescue missions, inspection of confined spaces, and environmental monitoring. The successful performance in these tests reflects the project's real-life applicability, showcasing how the combination of biomimetic design, 3D printing, and IoT can address current challenges in robotics, providing a reliable and efficient solution for complex and dynamic environments.

Fig. 4: Final design

Fig. 4. Final design

Floor type. The initial experimentation involved testing the robot's locomotion performance on various types of terrain, including stone asphalt, wood, pavement, grass, and sand. The results of the experiments, as summarized in the table, indicate minimal variations in the vertical displacement (zaxis) of the robot across different types of terrain, with measurements ranging from 173 mm to 176 mm . This consistency suggests that the robot's peristaltic locomotion mechanism is relatively robust and effective across a range of surface textures and conditions.

However, it's essential to consider potential factors that may influence these results, such as surface roughness, traction, and adhesion properties. For instance, while the robot may exhibit consistent vertical displacement on different surfaces, its traction and overall stability may vary, affecting its ability to maintain a straight trajectory or navigate obstacles effectively. Additionally, environmental factors such as moisture levels, temperature, and surface irregularities could impact the robot's performance and should be considered in further analyses.

Furthermore, further investigation into the specific mechanisms underlying the robot's locomotion on different surfaces could provide valuable insights into its adaptability and efficiency. Analyzing the interaction between the robot's motion and surface properties, such as friction, adhesion, and deformation, could help optimize its design and performance for specific applications and environments. 4

Overall, while the initial experimentation yields promising results regarding the robot's locomotion across various terrains, further analysis and testing are warranted to fully understand its capabilities and limitations in real-world scenarios. By systematically evaluating the robot's performance under different conditions and refining its design based on empirical data, we can ensure its effectiveness and reliability in practical applications.

Legs size. The subsequent experiment involved modifying the size of the robot's legs by increasing their length, with the intention of potentially improving stability or traction on various surfaces. However, the results indicated that despite the larger leg size, the robot exhibited decreased speed

Fig. 5: Temperature Sensor

Fig. 5. Temperature Sensor

during locomotion. This finding suggests a trade-off between leg size and locomotion speed, where larger legs may provide enhanced stability or surface contact but also increase friction or resistance, resulting in slower movement.

Analyzing these results, it becomes evident that the relationship between leg size and locomotion speed is complex and multifaceted. While larger legs may offer advantages in certain aspects such as stability or weight distribution, they may also introduce limitations in terms of agility, maneuverability, and overall locomotion efficiency. Therefore, the decision to modify leg size should be carefully considered and balanced with other design factors to achieve optimal performance for the specific application and environment.

Furthermore, this experiment highlights the importance of iterative testing and refinement in robotics design. By systematically evaluating the impact of design modifications on performance metrics such as speed, stability, and efficiency, engineers can gain valuable insights into the underlying mechanisms and trade-offs involved. This iterative approach allows for the identification of optimal design parameters and the development of more robust and effective robotic systems.

Overall, while increasing leg size may offer potential benefits in terms of stability or surface contact, it's essential to carefully evaluate its impact on locomotion speed and other performance metrics. By conducting systematic experiments and analysis, engineers can make informed design decisions and optimize robotic systems for a wide range of applications and environments.

Conclusion

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In conclusion, the development and experimentation with the biomimetic robot employing peristaltic locomotion and integrated IoT technologies represent significant advancements in the field of robotics. The integration of 3D printing facilitated the fabrication of customized components, offering costeffective solutions and enabling iterative design improvements. Furthermore, the incorporation of IoT capabilities allowed for real-time data collection, remote monitoring, and predictive maintenance, enhancing the robot's functionality and adaptability. However, experimental results highlighted the importance of carefully balancing design modifications, such as leg size adjustments, to optimize per-
formance metrics like speed and stability. Moving forward, continued research and development in biomimetic robotics, combined with IoT integration and 3D printing advancements, hold promise for addressing complex challenges and unlocking new possibilities in various applications, from environmental monitoring to industrial automation and beyond. By leveraging interdisciplinary approaches and iterative design methodologies, the frontier of robotics continues to expand, offering innovative solutions to meet the demands of a rapidly evolving technological landscape.

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The original version of this paper is available on https://www.scientific.net/EI.14.71.pdf

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The original version of this eBook is available on https://www.scientific.net/book/engineering-innovations-vol-14/978-3-0364-1777-6

Engineering Innovations, ISSN: 2813-1002, Vol. 14, pp 85-98

doi: 10.4028/p-XUQf31

© 2025 The Author(s). Published by Trans Tech Publications Ltd, Switzerland.

Submitted:
2024-11-29

Revised:
2025-01-14

Accepted:
2025-01-22

Online:
2025-04-04

Implementation of an IOT Sensor Network and Machine Learning to Measure the Water Quality

The original version of this paper is available on https://www.scientific.net/EI.14.85.pdf

Jose Fernando Alarcón Madrid , Owen Josue Paz Quintanilla , and Martin Martinez-Rangel

Universidad Tecnológica Centroamericana, San Pedro Sula, Honduras

Universidad Autonoma de Morelos, Cuernavaca, México fer.alarcon002@unitec.edu, owenjosue09@unitec.edu, mmtzr@uaem.mx

Keywords: Internet of Things, Machine Learning, Sensor Network, Water Quality

Abstract

Lagoons have a great importance for society, and activities such as fishing or tourism are essential for these areas, for this reason it is important to have a monitoring system in terms of water quality. The central axis of this project was the design and implementation of a sensor network based on the Internet of Things, collecting data using an ESP32 and the Thingspeak platform for data visualization and storage. Data is analyzed using MATLAB, allowing to obtain an estimation of the water quality index of Laguna Jucutuma indicating an average rating of 40, as well as using Machine Learning techniques to obtain a models with an error margin below .

Introduction

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Laguna Jucutuma is one of the most important wetland ecosystems for the region and for the country in general, being habitat for different species of flora and fauna, as well as economic activities such as fishing or tourism. However, in recent years it has been strongly affected by the pollution that surrounds it, either by construction in nearby areas, sewage drainage, forest fires or the cultivation of African palm.

Currently, it is common knowledge that lakes and lagoons are polluted daily. In contrast to this problem, it is important to use a network of sensors to measure variables such as: temperature, turbidity, pH , TDS or amount of dissolved solids and oxidation potential. These variables are sensed and sent to an online platform called ThingSpeak, where they will be stored and historical records can be observed in the form of graphs, in order to have an easier data analysis. Subsequently, these data will be analyzed using MATLAB and where a water quality index will be obtained to finish with the use of a form of Artificial Intelligence, for the prediction of useful data within the network.

One of the differential factors of the project is its low power consumption. Being designed and tested so that a 6W power panel is enough to energize the whole electronic circuit; using sensors that can be energized between 3.3 V and 5 V . In this way the prototype and subsequent buoy will be self-sufficient to have a continuous data measurement. With the implementation of long-range communication modules (LoRa), data collection can be done at any point within the range of the module, allowing an easy connection to the Internet for data visualization on the ThingSpeak platform.

Theorical Framework

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Water quality analytics is essential for defining and monitoring the safety and health of water sources, requiring evaluation of chemical, biological, and physical variables to meet standards for drinking, recreation, and ecosystem preservation [1]. Maintaining water quality is challenging, but using sensors and technologies like AI and computer vision can help collect essential data and improve natural resource management techniques [2]. Exploiting the potential of Machine Learning and the Internet of Things (IoT) to solve environmental problems has gained popularity in recent years. Water quality analysis and forecasting, a crucial area for human wellbeing, agriculture, and ecological sustainability, exemplifies this trend [3]. Water and air are vital for Earth's quality of life. Monitoring pH , temperature, and pollution in aquatic resources is crucial to reduce their impact.

Sensor network

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Wireless sensor network (WSN) systems are highly regarded for water monitoring due to their real-time, wide-area monitoring capabilities. The deployment of numerous sensor nodes is facilitated by the current affordability and compactness of WSN sensors and wireless communication modules [4]. These devices enable grab sampling reporting and facilitate quantitative analytical approaches at specific times and locations, addressing cost concerns by establishing a cost-effective network that offers valuable qualitative water quality information, thereby supporting informed decision-making [5]. The system's applications extend to live water quality assessment with minimal complexity, energy consumption, and cost, utilizing RF frequencies for direct data transmission without local power supply or node-level signal processing. The system's design and evaluation leverage low-cost printed circuit board (PCB) technology and traditional microwave architecture [6]. The integration of sensor networks is crucial for effective monitoring systems, particularly for collecting data on variables necessary to establish a water quality index for specific areas of Laguna Jucutuma, which also informs predictive modeling efforts.

Water quality

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Water quality indicators are recognized for their ability to promptly and accurately detect annual cycles, regional and temporal variations, and current trends in water quality, even at low concentrations [7]. A comprehensive detection system capable of monitoring pH , free chlorine , temperature, and micro-organic contaminants necessitates a novel design approach to develop an integrated, cost-effective system that can assess multiple water quality parameters simultaneously [8]. In addition to physicochemical parameters like pH , turbidity, temperature, oxidation-reduction potential (ORP), electrical conductivity, and flow rate, microbiological measurements offer a faster and more economical means to evaluate water quality [9]. Traditional monitoring methods reliant on sample collection and laboratory analysis are costly and lack real-time data capture capabilities. Addressing these limitations can significantly enhance water quality monitoring and management capabilities.

Internet of Things

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The Internet of Things (IoT), also referred to as the Industrial Internet, Internet of Everything, or simply IoT, represents a transformative paradigm that envisions a globally interconnected network of devices and objects [10]. An IoT-based water quality testing network addresses this concept by continuously monitoring and evaluating water properties using IoT solutions tailored for water quality monitoring [11]. IoT enables real-time access to information from physical objects, fostering innovative services that enhance productivity and efficiency. Key technologies associated with IoT include ubiquitous computing, RFID, wireless sensor networks, and cloud computing [12]. IoT proves crucial for monitoring systems due to its inherent advantages, particularly when integrated with sensor networks that provide continuous data collection. This setup ensures ongoing monitoring of water parameters, accessible globally, which is beneficial for both public awareness and scientific research on water quality management, including treatment plants.

LoRa

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For Internet of Things applications requiring low-power, battery-operated embedded devices that must transmit small amounts of data over great distances quickly, the LoRa (Long Range) family of wireless communication is gaining popularity [13]. LoRa is an attractive option for smart sensing in industrial and civil infrastructure applications such as environmental monitoring, because to its extended range and low power consumption [14]. LoRa can communicate reliably over long distances using unlicensed ISM frequency channels. This physical layer contains FEC and CSS. LoRa has emerged as the most popular physical layer for Low Power Wide Area Networks due to its versatility and strength. As a result, LoRaWan was designed to work at higher layers [15]. Continuing with the technology for the internet of things, LoRa communication allows to have that distance factor. It is important that the buoy does not connect directly to the ThingSpeak platform to avoid possible coverage or connectivity problems. LoRa allows to send data from the buoy to a receiving device, which is in charge of uploading the data.

Machine learning

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Machine Learning is increasingly indispensable for analyzing data, predicting outcomes, and classifying patterns, especially given the burgeoning volume of aquatic environmental data [16]. Compared to traditional models used in water research, machine learning-based data-driven models excel in addressing complex nonlinear challenges. Leveraging statistical analysis and current data, machine learning plays a critical role in predicting water quality, contamination levels, and implementing preventive measures to maintain cleanliness [17, 18]. Machine learning models can effectively simulate pollutant movements and hydrological processes when sufficient large-scale datasets are available, facilitating the detection and monitoring of dissolved contaminants in water using sensors that measure changes in capacitance values [19]. When a data set with high capabilities is available, Machine Learning technologies can efficiently capture the movement of contaminants and hydrological processes with the integration of sensors can provide important data to identify and monitor a variety of dissolved contaminants in water.

Methodology

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This work consists of the design, integration and implementation of a water sensor network with two significant approaches, the first one is the IoT approach consisting of data collection and visualization in real time, and the second one is Machine Learning where different techniques were used to get the best predictive model of water quality index.

Approach

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A quantitative research based on data collection of the variables as potential of hydrogen, total dissolved solids, turbidity, temperature and oxidation-reduction potential collected by the sensor network. This data is analyzed with the use of MATLAB and using the weighted arithmetic water quality index method to estimate the quality of water. The approach focuses on the use of the water quality index to evaluate how the variables affect water quality.

Study area

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Laguna Jucutuma is situated in San Pedro Sula City, Cortés Department, Honduras, with geographical coordinates at approximately latitude and longitude. The lagoon is located in the northern part of the country, near the urban center of San Pedro Sula.

Fig. 1: Satellite Image of Laguna Jucutuma

Fig. 1. Satellite Image of Laguna Jucutuma

Applied techniques and tools

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Arduino: The use of the Arduino IDE allowed us to create a code in C++ for the microcontroller to allow data collection and integration to the cloud layer. In addition, it was used to perform AT commands to program the LoRa modules.

ThingSpeak: It is an online platform which allows users to store data in the cloud, visualize the data and perform data analysis within the online site.

MATLAB: Is a powerful tool that is widely used to analyze data. It was used to determine the water quality index and the use of Machine Learning techniques to create different linear regression predictive models.

Proteus: It is a useful CAD program that is used to design electronic circuits, simulate them and create PCB schematics and layouts to make electronic boards.

SolidWorks: Is a CAD that is used for 2D and 3D modeling for manufacturing, it was used to design a prototype.

Materials

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The materials used in the sensor network required to be waterproof and submergible, different interfaces were used to isolate the electronic board with the sensors. Also, the interfaces and microcontroller required a voltage function of 3.3 V or 5 V to be powered by a solar panel.

ESP32: Is the main microcontroller programmed to make data collection via ADC and command the LoRa module to send data to other ESP32 which serves as the access point to the cloud.

pH sensor: Is a sensor that detects ions of hydrogen and by electrical potential measures the pH of a body of water, determining if the water is alkaline or acid.

TDS sensor: Total suspended solids sensors that indicate how many soluble solids are dissolved in water.

Turbidity sensor: A sensor which uses light to detect suspended solids in water, measured to indicate the opaqueness of water.

DS18B20: A digital temperature sensor that is suited to work in the water.

ORP sensor: Submergible sensor used to measure the oxidation-reduction potential of water.

Solar panel: A panel was used as the main power supply of the sensor network.

3.7V LiPo Battery: Is the secondary power supply of this project, the battery supply is 3.7 V and its capacity is 2000 mAh .

Solar Power Manager: Is a high efficiency solar power management module that gives us controllable 5V 1A outputs and provides a space to connect the solar panel and connect a LiPo battery to supply the board and charge while the solar panel is active.

RYLR993 Lite: Is a LoRa module which acts as a transceiver, two of them were used, one as transmitter and other as receiver, it is low-consumption, and the communication range is up to 20 km .

Antenna: Is an antenna that operates with radiofrequency usually in a range of 868 to 915 MHz .

Data collection and sampling

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For this project the analogue-digital converter was used to convert voltage readings to understandable parameters except for the DS18B20 temperature sensor, which is digital. The ESP32 ADC converter is of 12-bit resolution and with coding provided by the manufacturer which includes voltage conversion, sensor calibration and temperature compensation to get accurate values from the respective sensors.

Two LoRa modules were used to transmit and receive data, with the use of UART communication with the microcontroller and the transmitter, AT commands are used to communicate with the receiver via radiofrequency, in Laguna Jucutuma it was used a frequency of 915 MHz which is the frequency band set aside for ISM (Industrial, Scientific, and Medicine) in America. The receiver was connected to another microcontroller located in a Wi-Fi access point available to upload to the ThingSpeak server.

The data sampling for this study was conducted over a period from May 1st, 2024, to June 3rd, 2024. During this timeframe, Laguna Jucutuma was systematically monitored to gather comprehensive water quality data. To ensure a robust dataset representing various conditions within the specified period, data collection occurred on 20 different days. On each of these days, sampling was performed between 11:00 AM and 1:00 PM GMT-6. This specific time window was chosen to maintain consistency and reduce variability caused by diurnal changes in water quality.

Data samples were taken at intervals of 40 seconds, resulting in approximately 100 data samples being collected each day. This high-frequency sampling approach yielded a total of around 2000 samples over the entire period. The consistent timing and frequent sampling intervals were designed to capture detailed and reliable measurements, providing a comprehensive understanding of the water quality dynamics in Laguna Jucutuma during the study period.

Data processing

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The first step in data processing is the pre-processing which consists in the preparation of data, with ThingSpeak we download a .csv file that contained the whole dataset, in MATLAB a code was developed to clean data and normalize to perform better Machine Learning models.

Water Quality Index

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Water Quality Index (WQI) is a valuable tool in water resource management, helping to monitor and improve water quality through systematic evaluation and informed decision-making by converting different parameters into a single value. This simplification makes it possible to compare the water quality in various locations and times by condensing vast amounts of data into an understandable manner. WQI assists in clearly communicating the state of water quality to management and the public.

The Weighted Arithmetic Water Quality Index (WAWQI) is a widely used method to assess overall water quality by providing a consolidated view of water quality, considering the relative importance of each parameter through assigned weights. In order to calculate the water quality index, the next formula was used [20]:

where is the unit weightage for each parameter denoted by the following formula:

is the recommended standard of each parameter and k is a proportional constant calculated by:

following Eq. 1, is the subindex of each parameter which is determined by:

is the monitored value of each parameter and is the ideal value of each parameter values can be visualized in [20]. After calculating the result can be classified according to Table 1:

Table 1: WQI Status

WQI | Status
0-25 | Excellent
26-50 | Good
51-74 | Poor
76-100 | Very Poor
>100 | Unfit

Machine learning techniques

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Support Vector Machine (SVM) Regression

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SVM regression leverages the same core principle as SVM classification - finding a hyperplane that maximizes the margin between the predicted values and a margin of error. In regression, this margin of error represents the allowed deviation between predicted and actual values. Unlike linear regression, SVM regression can handle non-linear relationships between features and the target variable by employing kernel functions that map the data into higher-dimensional spaces. However, SVM regression can be computationally expensive for large datasets and requires careful parameter tuning for optimal performance.

Gaussian Process Regression

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Gaussian Process (GP) regression is a probabilistic approach that assumes the data follows a Gaussian distribution. It utilizes a kernel function to model the covariance between data points, enabling predictions with uncertainty quantification. This provides information about the model's confidence in its predictions. GP regression is flexible and can handle complex relationships but can also be computationally expensive for large datasets and may require expertise for effective parameter selection.

Kernel Approximation

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Kernel Approximation methods represent a family of techniques that employ kernel functions to capture non-linear relationships in the data. These methods often involve approximating a complex function using a combination of simpler basis functions. While offering more flexibility than linear regression, they can be computationally expensive for high-dimensional data and require careful selection of the kernel function and its hyperparameters.

Neural Networks

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Artificial Neural Networks (ANNs) are powerful tools capable of modeling complex relationships between features and the target variable. In regression tasks, ANNs learn these relationships through a series of interconnected layers of processing units (neurons). By adjusting the weights and biases of these connections during training, ANNs can capture intricate patterns in the data. However, ANNs require significant training data and computational resources, and their interpretability can be challenging due to their complex internal structure.

Model performance

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The evaluation of a machine learning model's performance is a crucial step in the development process. This section explores various metrics employed to assess the efficacy of the proposed models: Mean Squared Error (MSE), Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), and R-Squared (Coefficient of Determination) [21].

-Squared

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Shows how much the dependent variable's variance that the model's independent variables can account for. A higher number indicates a better fit. Its range is 0 to 1 . It is important to note that R Squared does not directly reflect the magnitude of errors but rather the model's ability to capture the overall trend in the data.

MSE
Calculates the average squared difference between the predicted values and the actual values, lower MSE values signify a better model fit, with a value of 0 indicating a perfect fit. However, MSE is sensitive to outliers, as large errors are squared and contribute more significantly to the overall error.

RMSE
Is easier to interpret in the same units as the predicted values, making it a more intuitive metric. However, it inherits the limitations of MSE regarding sensitivity to outliers.

MAE
Determines the mean of the absolute variations between the values that were anticipated and those that were observed. Unlike MSE, MAE is less sensitive to outliers, providing a clearer picture of the average magnitude of errors.

MAPE

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Is beneficial for comparing errors across different data ranges, particularly when dealing with percentages or proportions. However, it is not ideal for scenarios with zero actual values, as it can lead to division by zero errors.

In these formulas, is the total number of observations in the dataset, is the monitored value of the dependent variable, is the predicted value of the dependent variable and is the mean value of .

Study methodology

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In this study, we employed a hierarchical method to systematically evaluate and optimize a threelayered water quality sensor network system. This system architecture is designed to enhance the monitoring and management of water quality parameters across various environments. The three distinct layers-device, edge, and cloud-each contribute uniquely to the overall functionality and performance of the sensor network.

A. Device Layer

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The device layer forms the foundational level of the sensor network, encompassing physical sensors, power supply mechanisms, and microcontroller units. These components are deployed directly within the water bodies or monitoring points to capture real-time data on crucial parameters such as pH levels, total dissolved solids, turbidity, temperature and oxidation reduction potential. Calibration processes within this layer ensure the accuracy and reliability of sensor readings, crucial for generating precise environmental data.

B. Edge Layer

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Situated between the device and cloud layers, the edge layer acts as a pivotal hub for data aggregation and preliminary processing. Here, data from multiple sensors is consolidated and formatted before transmission to the cloud for further analysis and storage. Integration of technologies like

Long-Range (LoRa) communication modules enables efficient and low-power data transmission over extended distances, facilitating seamless connectivity between remote sensor nodes and the central cloud infrastructure.

C. Cloud Layer

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At the apex of the hierarchy lies the cloud layer, where vast volumes of sensor data are stored, managed, and analyzed. Cloud-based storage solutions provide scalability and flexibility, accommodating the continuous influx of real-time data streams from distributed sensor nodes. These insights can inform timely decision-making processes related to water quality management, resource allocation, and environmental remediation strategies.

Fig. 2: Hierarchical Method Framework

Fig. 2. Hierarchical Method Framework

Validation methodology

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To validate the effectiveness and reliability of the three-layered water quality sensor network system, encompassing the device layer, edge layer, and cloud layer, we implemented a rigorous validation methodology. This approach aimed to assess the accuracy of sensor readings, the efficiency of data transmission, and the robustness of data analysis and decision-making processes facilitated by the cloud-based infrastructure. A. Sensor Calibration and Validation

Calibration Procedures: Sensors at the device layer underwent meticulous calibration procedures to ensure consistency and accuracy in measuring water quality parameters. Calibration standards were established based on laboratory benchmarks and manufacturers information.

Validation Tests: Validation tests were conducted under varying environmental conditions to verify sensor accuracy and reliability that included comparison against standardized reference measurements.

B. Edge Layer Data Transmission Evaluation

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Resilience tests were conducted to simulate scenarios of network congestion or interference, ensuring the robustness of data transmission mechanisms in adverse conditions.

C. Cloud Layer Data Collection and Machine Learning Evaluation

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Data Collection: We ensured that data was sent to ThingSpeak by guaranteeing the microcontroller an access point to upload data to the cloud.

Analytical Accuracy: Machine learning models and analytical algorithms deployed in the cloud layer were subjected to validation tests to assess their predictive accuracy and reliability.

Design

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For the correct operation, a design was made around three important parts or stages for the correct operation: Electronics, Communication and Data Processing.

Electronics

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After selecting the variables to be measured and selecting the corresponding sensors, an electronic circuit is designed together with a microcontroller for data processing. The information is collected by the five sensors and then processed by the ESP32 microcontroller, which is then sent to another microcontroller through the REYAX RLYR 993 Lite module. The emitter device will be powered with 5 V from the solar panel, having a maximum load capacity of 1.2 A , being more than enough for the 147.5 mA consumed by the emitter buoy.

In turn, the receiving device has the same module and controller, as well as five LED lights that indicate the receipt of the information. Once the two circuits are designed, the PCB design was done in the Proteus software.

Fig. 3: IoT Architecture

Fig. 3. IoT Architecture

Communication

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To send data, both the transmitting and receiving devices use the REYAX RLYR 993 Lite LoRa modules. These modules use LoRa (Long Range) technology and communicate by AT commands through the UART serial protocol and radio frequency, using the air as a means of transport, so in this section the devices are wireless. Subsequently, the transmitter device, upon receiving the data obtained by the sensors, the micro-controller is responsible for sending the data to the ThingSpeak platform, using the MQTT protocol (Message Queuing Telemetry Transport), a protocol that allows the sending of light messages, which travel through reduced bandwidths and designed for devices that occupy small codes and communication between machines, M2M, and IOT.

Data processing

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For this stage, ThingSpeak is in charge of graphing and saving the data; this allows to have a historical count of the information, as well as the ease of visualization of such data. MATLAB oversees using these data so that, by using Eq.1, a response can be generated, and, in turn, predictive models can be generated from the data obtained.

Results

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This section shows the results obtained during the whole period, as well as the manufacture of elements described in the design part. Starting with the electronic part, as shown in Fig. 7, a PCB board was manufactured where the 5 sensors, the microchip, the radio frequency module and the power supply from the solar panel are concentrated. This part was fundamental since in this way there is a better conduction of the information, this due to the medium through which the energy circulates, leaving aside the cables and working on copper tracks. This also avoids loss of information, so the measurements are more accurate.

Continuing with the manufacturing section in the electronic part, as can be seen in Fig. 8, a PCB board was also made for the receiver device; device that is responsible for sending data through IoT, so it is crucial to maintain a stable and adequate current and voltage.

Fig. 4: Final Prototype Deployment

Fig. 4. Final Prototype Deployment

For the communication part, Fig. 8 shows an antenna which is used to transport the radio frequency information from the transmitting device located in the buoy to the receiving device. For data processing, the data is sent from the module to the ESP32 with serial communication (UART). Subsequently, as shown in Fig. 9, these data are sent to ThingSpeak, where they can be displayed in the form of graphs, facilitating a study of these same. This platform also offers the benefit of saving these values, to be later downloaded and used by MATLAB.

Fig. 5: ThingSpeak Interface

Fig. 5. ThingSpeak Interface

In the final stage of the project, in the data processing, all the collected information is used in MATLAB, where it shows us different results, in the first place it shows statistical information of the collected data of each variable, such as the average, the median or the standard deviation. Different machine learning models were used to find the most suitable model. As can be seen in Table 2 the model with better fit and less magnitude of errors was the Gaussian Process, SVM showed very good efficacy but not better than Gaussian Process, both ANN and Kernel were good, but they were susceptible to errors.

Table 2: Model Performance Results

Model | RMSE | R-Squared | MSE | MAE | MAPE
Gaussian Process | 0.78544 | 0.98 | 0.61692 | 0.33159 | 0.80%
Support Vector Machines | 0.91823 | 0.98 | 0.84315 | 0.55446 | 1.30%
Neural Networks | 1.133 | 0.97 | 1.2836 | 0.68593 | 1.70%
Kernel Approximation | 1.3339 | 0.95 | 1.7792 | 0.88599 | 2.10%

Fig. 6: (a) Rational Quadratic GPR Model, (b)Fine Gaussian Model, (c)Wide Neural Network Model, (d)Least Squares Regression Kernel Model

Fig. 6. (a) Rational Quadratic GPR Model, (b)Fine Gaussian Model, (c)Wide Neural Network Model, (d)Least Squares Regression Kernel Model

Around the 20 days the range of the WQI was between 29 and 52, at the beginning of May, water quality index was at 40 and started increasing due to the rise of air pollution and smog, the highest value was registered in May 15. At the beginning of June, WQI index was low because air pollution turned better. Other studies implement statistically based methodologies with which a good level of accuracy is achieved but are dependent on people-based processes [22, 23]. In this case we propose a device capable of monitoring water quality by applying artificial intelligence techniques capable of operating in site. This project is low cost and can be developed in different lakes and rivers where water quality is having adverse effects on nature.

Fig. 7: Water Quality Index rate per day

Fig. 7. Water Quality Index rate per day

Conclusions

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The implementation of IoT sensors and Machine Learning allows the collection and analysis of real-time data on water quality, improving environmental management in Laguna Jucutuma, facilitating the protection and prevention of pollution problems in the lagoon.

The maximum value of the water quality index was calculated as 52 on May 15 when the air pollution was at its highest point and the lowest index of 29 was recorded on June 6.

The Gaussian Process regression learner model was the most fitted model with an RMSE of -squared of 0.98 , MSE of 0.61692 , MAE of 0.33159 and MAPE of .

References

The original version of this paper is available on https://www.scientific.net/EI.14.85.pdf

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The original version of this eBook is available on https://www.scientific.net/book/engineering-innovations-vol-14/978-3-0364-1777-6

Engineering Innovations, ISSN: 2813-1002, Vol. 14, pp 99-115

doi: 10.4028/p-wcnTd6

© 2025 The Author(s). Published by Trans Tech Publications Ltd, Switzerland.

Submitted:
2024-08-27

Revised:
2024-12-02

Accepted:
2024-12-04

Online:
2025-04-04

Implementation of Boosting Algorithms in Predicting Air Quality Index of South Indian Cities

The original version of this paper is available on https://www.scientific.net/EI.14.99.pdf

Deepasikha Mishra , Ansuman Sahu , Sreajan Naman

School of Computer Science and Engineering, VIT-AP University, Andhra Pradesh, India

deepasikharesearch@gmail.com, ansumansahu264@gmail.com, namansreajan@gmail.com

Keywords: Air Pollution, Air Quality Index (AQI), XGBoost, LightGBM, Ensemble Stack.

Abstract

This paper addresses the critical issue of atmospheric pollution in India, underscoring the necessity for precise predictive analytics of Air Quality Index (AQI) data for effective pollution control. The study delineates the etiological factors and substantial health hazards correlated with air pollution, encompassing elevated mortality rates, respiratory and cardiovascular diseases, and mental health complications. The AQI is presented as a necessary component for converting complex air quality data into a single, easily understandable metric. The principal aim of this research is to facilitate effective pollution control through real-time AQI monitoring and precise future predictions for timely interventions. To attain this objective, the research employs the use of boosting algorithms, like extreme gradient boosting (XGBoost), light gradient boosting machine (LightGBM), and an ensemble stack of XGBoost and LightGBM for AQI prediction of four South Indian cities Amaravati, Bengaluru, Chennai, and Hyderabad. The results presented in this paper are based on daily data of the aforementioned cities, collected from the Central Pollution Control Board (CPCB) website, Government of India, covering the period from May 31, 2019, to November 22, 2023. The performance of the deep learning models on the data was found noteworthy, with consistently high scores and low root mean squared error (RMSE), exhibiting their efficacy in providing accurate results. By merging technological innovation with machine learning capabilities, the research aims to equip decision-makers with actionable insights for informed pollution mitigation strategies, promoting a more sustainable environment.

1. Introduction

The original version of this paper is available on https://www.scientific.net/EI.14.99.pdf

Atmospheric air constitutes a vital ecological resource essential for the survival of biotic entities on Earth. Humans, animals, and plants depend on atmospheric quality, rendering the preservation of pristine air paramount for life sustenance. According to the Blacksmith Institute's 2008 report, the globe's most severely polluted locales are marred by deteriorating urban air quality and pervasive indoor pollution [1]. Outdoor air pollution leads to around 6.7 million deaths globally each year, while indoor air pollution also results in significant number of premature deaths annually in certain regions. [2,3]. Various sources, including the use of unclean fuels for household purposes in developing countries, emissions from furniture and construction materials, and emissions from microorganisms cause indoor air pollution. Poor ventilation and additional indoor sources make indoor air pollution more complex and often more concentrated than outdoor air pollution [3,4]. Addressing the detrimental impacts of air pollution necessitates a dual focus on urban air quality and indoor pollution abatement. India, as a rapidly developing nation, grapples with escalating air pollution due to swift urbanization and industrial proliferation. The burgeoning population, vehicular emissions, and industrial effluents are accelerating atmospheric degradation. Exposure to polluted air precipitates both acute and chronic health repercussions. Empirical studies have substantiated that air pollutants, such as fine particulate matter (PM2.5) and other noxious substances, elevate mortality rates and induce respiratory, cardiovascular, and psychological disorders. Acute exposure to polluted air correlates with heightened mortality from all causes, including cardiorespiratory conditions, especially among the elderly, while young children exposed to PM2.5 exhibit increased anxiety symptoms [7,8]. Data analytics and machine learning paradigms have emerged as robust methodologies for enhancing air quality prognostications, offering superior accuracy in pollution
assessment and forecasting. Researchers have extensively utilized data-driven and machine learningbased models for evaluating the quality of air. The AQI serves as an effective communicative instrument, translating complex air quality data into accessible terms for public comprehension and action. The combination of AQI monitoring, accurate predictions through machine learning, and a deep analysis of air quality patterns positions this study as a significant step toward addressing the air pollution crisis in India. The remaining segments of the paper is organized as follows: the literature review related to the work is covered in Section 2, the research methodology is outlined in Section 3, the results are presented in Section 4, and the conclusions are provided in Section 5.

2. Literature Review

The original version of this paper is available on https://www.scientific.net/EI.14.99.pdf

The unprecedented air pollution rise in India, spurred by rapid urbanization and industrialization, mandates an exhaustive examination of contemporary air quality assessment and management research. This literature review aims to synthesize the research findings delving into the detrimental effects of air pollution in Indian cities, highlighting the significant threats to public health and the environment. Integrating machine learning (ML) algorithms emerges as a promising strategy for predicting and mitigating air pollution, specifically focusing on enhancing our understanding of the intricate dynamics of pollutants.

2.1 Health Hazards

Air pollution is emerging as a significant concern which is impacting public health. Studies have shown a correlation between exposure to atmospheric pollutants and the incidence of acute lower respiratory infections, chronic obstructive pulmonary disease, asthma, and pulmonary carcinogenesis . PM10 and PM2.5 have been identified as major contributors to deleterious respiratory health outcomes, even at minimal pollutant concentrations [31,32]. In India, the situation is particularly alarming, where high levels of particulate matter pose a grave concern, necessitating policy actions to reduce pollutants and attain immediate health benefits. Controlling outdoor air pollution in India can lead to significant health benefits, including reductions in morbidity and mortality. However, the distribution and extent of these benefits vary widely across geography, time, and populations. Climate change-driven temperature increases exacerbate air quality, but climate solutions, such as clean and renewable energy and cool roofs, can improve air quality and health. Research indicates that higher air quality is intricately linked to an improved quality of life, which offers potential avenues for mitigating the health risks associated with air pollution [33]. Children are especially susceptible to the harmful effects of air pollution on their respiratory health, emphasizing the need to enhance air quality. Studies have shown that prolonged exposure to air pollution can adversely affect lung function development in children, reinforcing the importance of improving air quality [34,35]. Additionally, reducing PM2.5 levels has been associated with decreased medical costs and less loss of working and living time for patients, demonstrating the health benefits of air quality improvement [36]. Thus, it is essential to have efficient air quality management planning in place to mitigate the negative consequences of air pollution on human well-being.

2.2 Monitoring and Analysis of AQI

Monitoring and analyzing the AQI for comprehending pollution trends is of significant importance. A generative time-series model based on a recurrent extension of the variational autoencoder (VAE) has been proposed by researchers to forecast major pollution indicators with high efficiency [42]. The AQI is a metric widely used for recording and understanding pollution trends. Various machine learning algorithms have been proposed for predicting air contamination and analyzing the AQI [9,37,38,39]. Sensor-based networks have been developed to monitor air quality parameters and predict a locality's sustainability. In addition, dispersion models and control systems help understand the distribution and dynamics of pollutants and control air pollutant concentrations. Incorporating satellite data alongside statistical and deep learning techniques for AQI forecasting during COVID-19 lockdowns have substantially reduced air pollutant levels [43]. These studies have demonstrated the efficacy in AQI forecasting by implementing machine learning, achieving high
accuracy and outperforming other models. Researchers have investigated the air quality in Bangladesh during lockdown periods, revealing a strong correlation between COVID-19 spread and air pollution reduction [44]. Similarly, the impact of the lockdown on air quality in Henan, China, has been studied, and a decrease in pollutants during the lockdown period has been observed [45]. Furthermore, an analysis of the spatial and temporal variations in air quality measures during lockdown over the Indo-Gangetic Plain has shown a decline in pollutants and improved regional air quality [46]. Analyzing the trend of air pollutant concentrations can be challenging, mainly when dealing with data from monitoring networks of varying lengths. However, advanced monitoring systems incorporating satellite data and machine learning approaches offer a nuanced understanding of pollution trends. This understanding paves the way for designing effective abatement strategies to control air pollution levels.

2.3 Climate Change

Climate change and air pollution have complex and intertwined interactions, with significant environmental and public health consequences. Studies have shown that in the second half of this century, temperature changes and emissions of biogenic volatile organic compounds (VOCs) are projected to increase ozone concentrations, and addressing climate change can directly and indirectly reduce air pollution [47]. However, distinct policy levers are required to tackle both issues. Future climate scenarios show improved pollutant concentrations due to reduced emissions despite higher temperatures and lower precipitation. Climatic variables such as temperature, rainfall, wind, and humidity play a pivotal role in air pollution by affecting pollutant strength, transportation, and dispersion. Researchers have emphasized the need to integrate climate change considerations into future air quality predictions and policy interventions, and they have highlighted the need for improved tools to assess the combined implications of addressing air quality and climate change. Current Integrated Assessment Models (IAMs), which are commonly utilized in policy development, often employ global or regional marginal response factors to assess the air quality impacts of climate scenarios [48]. Still, this approach may lead to inaccurate conclusions. To address this gap, researchers have developed computationally efficient methods to measure the air quality impacts. The convergence of machine learning with air quality research highlights the potential of advanced technologies in facilitating more precise predictions and epidemiological analyses, shaping the trajectory of future research and interventions [9,37,38]. Extreme weather events, which are becoming increasingly common due to climate change, contribute to air pollution through events such as wildfires, releasing large amounts of pollutants into the atmosphere [49,52]. This relationship is not unidirectional, as certain pollutants, such as black carbon, can exacerbate climate change by absorbing sunlight and contributing to atmospheric warming. Recognizing these connections is essential for developing comprehensive plans addressing climate change and air pollution, ensuring effective environmental and public health management.

2.4 Usage of Predictive Analytics

Machine learning models have been extensively studied and utilized to improve air pollution research and prediction. They offer several advantages over conventional methods such as increased accuracy, simplified mathematical and statistical approaches, and improved analysis of air pollution data. Many machine learning techniques, such as Random Forest, SVM, Decision Trees, LS-SVM, and ANN, have been used in air pollution epidemiology [37,38]. These models have been employed to evaluate air quality for different regions and analyze air pollution data. XGBoost and LightGBM are two prominent machine learning algorithms that have been integrated with web modules to provide real-time air quality forecasts [9]. Regression models have been utilized to analyze air pollution data, showing noteworthy performances [37,38]. XGBoost has been shown to outperform deep learning techniques in estimating the AQI [39]. Random forest regression has demonstrated high accuracy and low RMSE in predicting pollution levels [40]. Ensemble voting models, such as the AQP-EDLMRA technique, which combines deep learning classification methods, have shown improved forecasting performance for air pollutant prediction [41].

3. Proposed Methodology

The original version of this paper is available on https://www.scientific.net/EI.14.99.pdf

The Air Quality Index (AQI) prediction for the four South Indian cities was performed using preprocessed datasets, which included seven air pollutant attributes: PM10, PM2.5, NO2, NH3, SO2, CO, and O3, along with daily AQI values and their respective AQI standards. Given that air quality datasets often contain missing or incomplete data, regression models are particularly useful because they are robust to such imperfections. These models can effectively manage missing data, providing reliable predictions even when some features are absent. Their flexibility and interpretability make them suitable for accurate and dependable AQI prediction. In this study, the regressor models used were XGBoost, LightGBM, and an Ensemble Stack combining both XGBoost and LightGBM to predict the AQI and evaluate the performance of these boosting algorithms. The overall process of the methodology is depicted in Fig. 1.

Fig. 1. Architectural Representation of the Research Work.

Fig. 1. Architectural Representation of the Research Work.

3.1 Data Collection

The research pertains to the study of air pollution and involves the use of data that has been downloaded from the CPCB website, under the Department of Environment, Forest, and Climate Change, Government of India [55]. The dataset comprises the air pollutant parameters of four South Indian cities, namely Amaravati, Bengaluru, Chennai, and Hyderabad. It contains seven distinct attributes of Air Pollutants as specified in the Indian System of AQI value calculation. The dataset is thus made ready to offer a comprehensive and detailed analysis of the air quality of these four cities, based on the aforementioned pollutant parameters.

3.2 Data Cleaning

During the data cleansing phase, extensive measures were implemented to address missing values and outliers within the dataset. The use of Excel formulas within the spreadsheet, specifically the IF and ISBLANK functions, allowed for a systematic approach to handling missing values. A datadriven approach was employed by filling missing values with the mean of the respective column values, as determined by Excel formulas such as AVERAGE and IF. Outliers were identified using Excel functions and statistical techniques and removed from the dataset, ensuring the dataset's accuracy and representativeness. Excel formulas were used to calculate the mean of the respective column values for the missing values and potentially inaccurate data points removed, and this mean value was then used to fill in the missing values. The dataset was consistently maintained throughout the data cleansing process to ensure its reliability and suitability for further analysis and implementation. The cleaned dataset was suitable for extracting meaningful insights, conducting statistical analyses, or applying machine learning algorithms, making it a valuable asset for research and decision-making.

3.3 Calculation of AQI and Evaluation of Air Standards Specified by CPCB

The daily AQI levels for the pollutants were calculated using a macro-enabled AQI calculator in Microsoft Excel, based on the pollutant concentrations extracted from the CPCB website data [55].

The formula used for the calculation of the AQI of the pollutant factors is:

where denotes the index for pollutant refers to the truncated concentration of pollutant , is the concentration breakpoint at or above is the concentration breakpoint at or below is the AQI value for , and is the corresponding AQI value for .

The Algorithm followed by the calculator for the calculation of the daily AQI is as follows:
STEP 1: Start
STEP 2: Calculate the average AQI for each pollutant over a 24 -hour period every day.
STEP 3: Determine the highest daily average AQI value among all pollutants to identify the most impactful pollutant.

STEP 4: Compute the greatest AQI value across all pollutants to assess the overall air quality status.

STEP 5: Determine the final AQI value.
STEP 6: End
The corresponding AQI categories were determined using a Python code that defined a range for each AQI Standard as specified by CPCB [55]. The air quality categories are provided in Table 1.

Table 1. Air Quality Index (AQI) and Standards Specified by CPCB.

Air Quality Index (AQI) | Air Quality Standard
0-50 | Good
51-100 | Satisfactory
101-200 | Moderate
201-300 | Poor
301-400 | Very Poor
401-500 | Severe

Appropriate Standards were assigned based on the calculated AQI values through the code. A new column was added to the Excel sheet to create a comprehensive dataset including the AQI Standards. This additional column provides a quick and precise classification of Air Quality Standards for each day, facilitating straightforward interpretation and analysis of the data. This methodology was used to prepare datasets for all four South Indian cities - Amaravati, Bengaluru, Chennai, and Hyderabad.

3.4 Machine Learning Models

XGBoost is an extremely efficient and scalable implementation of the gradient boosting framework. It leverages parallel processing capabilities, which significantly speeds up the training process, especially on multicore machines. XGBoost incorporates techniques such as L1 (Lasso) and L2 (Ridge) regularization to control overfitting and enhance the model's generalization capability on unseen data. The objective function of XGBoost consists of a loss function and a regularization term. The loss function, often a squared error term, measures the difference between the actual and predicted values, while the regularization term penalizes model complexity to prevent overfitting.

The objective function can be expressed as:

Here, and are regularization parameters, is the number of leaves in the tree, and is the vector of leaf weights. LightGBM is designed for high efficiency and scalability. It employs a histogram-based approach for decision tree learning, which reduces the number of split points by grouping continuous features into discrete bins, significantly speeding up the training process. LightGBM grows trees leaf-wise with depth limitation, splitting the leaf with the largest loss reduction among all current leaves. This strategy often results in better accuracy but may lead to overfitting if not properly regulated. The objective function in LightGBM is similar to XGBoost, incorporating loss and regularization terms. The loss function and regularization terms are adapted for the histogram-based and leaf-wise growth approaches, maintaining the balance between model complexity and performance. The ensemble stack of LightGBM and XGBoost involves training individual LightGBM and XGBoost models and then combining their predictions using a metamodel. This approach leverages the strengths of both models, yielding more accurate and robust predictions by capturing diverse patterns in the data. The combined predictions are typically achieved by averaging the predictions of the individual models or using a meta-model to learn the optimal combination of predictions. Experiments with various data split ratios (75-25, 70-30) revealed only marginal variations in scores and RMSE values for the LightGBM, XGBoost regressors, and their ensemble stack. Optimal performance was achieved with an split, where all three models demonstrated consistently high accuracy, emphasizing the significance of data-split ratio selection in influencing model effectiveness. In this work, an training and testing data split was used to train and evaluate the regression-based models. The performance of the regressor algorithms was assessed using and RMSE scores. The score, referred as the coefficient of determination, indicates the percentage of variation in the dependent variable that the independent variables explain. This metric ranges from 0 to 1 , where 1 represents a perfect fit. The formula for is:

where, represents the actual values, denotes the predicted values, and represents the mean of the actual values. The numerator expresses the sum of squared residuals, measuring the total deviation of the predicted values from the actual values while the denominator is the total sum of squares, measuring the total deviation of the actual values from the mean. A higher value indicates that a greater proportion of variance is captured by the model. The RMSE measures the average magnitude of prediction errors, with lower values indicating superior model performance. The formula for RMSE is:

where, represents the actual values and denotes the predicted values. RMSE provides a clear metric for comparing the accuracy of different models, as it directly quantifies the typical error magnitude in the predictions. The square root ensures that the error is measured in the same units as the original values for easier interpretation.

4. Results and Discussion

The original version of this paper is available on https://www.scientific.net/EI.14.99.pdf

This section provides a detailed examination of the AQI in four Southern Indian cities Amaravati, Bengaluru, Chennai, and Hyderabad. Southern India has diverse geographical features, including coastal areas, plains, hills, and forests. This diversity can lead to a wide range of pollution sources, such as industrial activities, vehicular emissions, and natural factors. The process of rapid urbanization and industrialization in cities like Chennai, Bangalore, and Hyderabad have significantly contributed to air pollution through vehicle emissions, industrial activities, and construction. Hence, monitoring air quality in urban and industrial areas can provide insights into pollution dynamics in these environments. Southern India experiences diverse climatic conditions, including monsoons,
tropical climates, and coastal influences. These climate patterns can affect the dispersion and accumulation of pollutants in the air, and studying how climate variability influences AQI values can enhance our understanding of the complex interactions between meteorological factors and air quality. Furthermore, Southern India has seen improvements in research and monitoring infrastructure, including air quality monitoring stations. Reliable data collection systems facilitate accurate estimation and prediction of AQI values; therefore the study takes into account these four South Indian cities to ensure comprehensive analysis. The results presented are based on the day-wise data available from May, 2019 to November, 2023 [55].

4.1 Analysis and Discussion

This section provides individual analyses of the AQI for each city, starting with Amaravati. The analysis indicates that Amaravati has an overall good air quality index over four years shown in Fig. 2. The data primarily consisted of good days, with only a few satisfactory days. This is a testament to the fact that Amaravati has a good green cover around the city, and the city's plantation has made the atmosphere and air quality quite good [12]. However, as the city is still developing as Andhra Pradesh's capital, it needs larger industrial endeavours with less pollution-related activities to ensure good air quality.

Fig. 2. Comparison of AQI Standard of Days from 2019 to 2023 in Amaravati

Fig. 2. Comparison of AQI Standard of Days from 2019 to 2023 in Amaravati

The analysis of pollutant concentrations in the air reveals that PM10 concentrations were higher than those of all other pollutants, with ozone being the second-highest pollutant contributor and Carbon monoxide being the least contributing pollutant. The report suggests that the dust generated from construction sites contributes as a major source of particulate matter. As a developing capital city, the region's ongoing construction or infrastructure development could significantly contribute to elevated PM10 levels shown in Fig. 3. Construction sites are known to have high concentrations of suspended particulate matter (PMs), and industrial activities and physical construction are significant sources of PM2.5-10 emissions [14]. Studies have shown that PM10 levels around cement industries and industrialized coastal cities are significantly higher than control areas [14]. The heavy metals and rare-earth elements in atmospheric particulate matter further confirm the impact of industrial emissions [26]. Moreover, Amaravati's warm and sunny climate could contribute to the photochemical reactions leading to elevated ozone levels [15]. Ozone is a secondary pollutant which forms in sunlight through complex photochemical reactions involving precursor pollutants, primarily nitrogen oxides and VOCs [16]. Increased sunlight provides the energy needed to drive these photochemical reactions, producing ozone from the precursors.

Fig. 3. Comparison of Concentration of Air Pollutants from 2019 to 2023 in Amaravati.

Fig. 3. Comparison of Concentration of Air Pollutants from 2019 to 2023 in Amaravati.

In Bengaluru, the Air Quality Index (AQI) shows that the city experiences a considerable number of good days, with some days being satisfactory and very few being moderately polluted. The city's green cover and numerous parks serve as natural filters, trapping particulate matter and absorbing pollutants, leading to improved air quality [25]. Furthermore, Bengaluru's moderate climate and geographical location may contribute to favourable air quality. The city's elevation, temperature, and weather conditions can impact how pollutants disperse and dilute, leading to cleaner air as shown in Fig. 4.

Fig. 4. Comparison of AQI Standard of Days from 2019 to 2023 in Bengaluru.

Fig. 4. Comparison of AQI Standard of Days from 2019 to 2023 in Bengaluru.

PM10, PM2.5, and Ozone were found to have a greater contribution to pollution than other pollutants [17,18,20]. Bengaluru, like many megacities, is facing a rise in air pollution issues due to urbanization, industrialization, and economic growth. Prolonged exposure to PM2.5 in Bengaluru has been linked with attributable deaths for chronic respiratory disease, coronary artery disease, stroke, and lung malignancy [17,19]. Exposure to O 3 has also been linked to attributable deaths from respiratory diseases [19]. A study also found that air pollution in Bengaluru is increasing due to rising temperatures and annual heavy rainfall [21]. The concentration of air pollutants has been shown in Fig. 5.

Fig. 5. Comparison of Air Pollutant Concentrations from 2019 to 2023 in Bengaluru.

Fig. 5. Comparison of Air Pollutant Concentrations from 2019 to 2023 in Bengaluru.

The Air Quality Assessments conducted for the city of Chennai have revealed air quality that ranges from good to moderate days, as shown in Fig. 6.

Fig. 6. Comparison of AQI Standard of Days from 2019 to 2023 in Chennai.

Fig. 6. Comparison of AQI Standard of Days from 2019 to 2023 in Chennai.

The primary contributors to air pollution in Chennai include PM10, PM2.5, NO2, and groundlevel ozone as shown in Fig. 7. Particulate matter, both fine and coarse, is primarily caused by vehicular emissions, industrial activities, and construction sites [22,23]. PM10 and PM2.5 are the major contributors to pollution in Chennai, and their sources include vehicular emissions, construction sites, and industrial activities. NO2 is mainly emitted from vehicle exhaust and industrial processes, while ground-level ozone is formed through complex chemical reactions. It is noteworthy that indoor and outdoor pollution are interrelated, with anthropogenic sources such as vehicle emissions and power plant effluents being the primary contributors to both [23,24]. Indoor sources of pollution in Chennai include cooking and heating activities, as well as the use of cleaning agents and personal care products [24].

Fig. 7. Comparison of Concentration of Air Pollutants from 2019 to 2023 in Chennai.

Fig. 7. Comparison of Concentration of Air Pollutants from 2019 to 2023 in Chennai.

Meanwhile, in Hyderabad, the air quality remains a pressing concern due to consistently high levels of PM10 and PM2.5 pollution as shown in Fig. 8. Particulate matter originates from a range of sources, like vehicle emissions, road dust, coal combustion in industries, burning of garbage, and secondary particulate matter. High concentrations of particulate matter pose a significant health risk to the population, with PM2.5 being associated with chronic obstructive pulmonary disease (COPD) [26]. The persistence of elevated particulate matter levels throughout the city emphasizes the need for sustained efforts to mitigate pollution sources and improve air quality management practices.

Fig. 8. Comparison of Concentration of Air Pollutants from 2019 to 2023 in Hyderabad.

Fig. 8. Comparison of Concentration of Air Pollutants from 2019 to 2023 in Hyderabad.

Despite the city experiencing a mix of good and satisfactory air quality days over the years, there are also instances of moderate, poor and very poor air quality, as illustrated in Fig. 9. This variability underscores the need for sustained efforts to mitigate pollution sources and enhance air quality management practices. Industrial activities prove to be a potential source of air pollution in Hyderabad, and targeted interventions are necessary to reduce their impact [28,29]. Additionally, the use of clean energy sources and the implementation of green transportation options in the city can help reduce the levels of air pollution.

Fig. 9. Comparison of AQI Standard of Days from 2019 to 2023 in Hyderabad.

Fig. 9. Comparison of AQI Standard of Days from 2019 to 2023 in Hyderabad.

4.2 Predictions by Machine Learning Models

This study examines day-wise air quality data from the Central Pollution Control Board (CPCB) website [55], covering the period from May 31, 2019, to November 22, 2023, to evaluate the performance of predictive deep learning models. After thorough preprocessing of raw data, comprehensive datasets were created for the four South Indian cities. The datasets were partitioned into training and testing subsets in an 80:20 ratio, with data randomly ordered to ensure that both subsets offered a balanced and representative sample of the overall dataset. This randomized
allocation was essential in preventing temporal biases that could occur in time-ordered splits, thus enhancing model robustness and generalizability across different periods. By randomly sampling data points for training and testing, this method ensured a comprehensive reflection of the data's inherent variability, thereby minimizing overfitting and providing a more accurate assessment of model performance. This split in data also demonstrated lower root mean square error (RMSE) and higher scores, outperforming other split configurations such as 75:25 and 70:30. The RMSE for both the training and testing sets was regularly monitored, and early stopping was applied to cease the training process when performance metrics showed signs of degradation, further reducing the risk of overfitting.

The study evaluates three deep learning architectures-XGBoost, LightGBM, and an ensemble stack combining both—on the prepared air quality datasets of four South Indian cities, using and RMSE as key performance metrics, as illustrated in Tables 2-5. LightGBM, with its histogram-based algorithm, provides advantages in computational efficiency and scalability, particularly with extensive datasets, whereas XGBoost is noted for its capability in modeling complex patterns within the data. The ensemble model, combining LightGBM and XGBoost, aimed to exploit the strengths of both models, producing a more robust predictor of air quality levels. State-of-the-art research highlights the effectiveness of XGBoost and LightGBM for urban air quality prediction. Notably, LightGBM's histogram-based approach has been shown to improve training speed and accuracy, as demonstrated in studies on air quality data from urban areas such as Beijing [53]. Moreover, the inclusion of key air quality indicators, such as PM2.5, nitrogen dioxide, and sulphur dioxide has shown to contribute to the precision of predictions [54]. XGBoost's proficiency in time series analysis has shown enhanced forecasting abilities by leveraging historical trends to predict future air quality levels [54]. Recent literature also supports the use of hybrid models that integrate regression techniques with XGBoost, effectively addressing overfitting and boosting predictive robustness [54].

Table 2. Comparison of Model Performance for AQI Prediction in Amaravati.

City | Algorithm | R² Score | RMSE
Amaravati | XGBoost | 0.9960 | 0.5668
LightGBM | 0.9968 | 0.5125
Ensemble Stack | 0.9965 | 0.5353

Table 3. Comparison of Model Performance for AQI Prediction in Bengaluru.

City | Algorithm | R² Score | RMSE
Bengaluru | XGBoost | 0.9870 | 2.0229
LightGBM | 0.9848 | 2.1846
Ensemble Stack | 0.9612 | 3.4924

Table 4. Comparison of Model Performance for AQI Prediction in Chennai.

City | Algorithm | R² Score | RMSE
Chennai | XGBoost | 0.9969 | 0.9540
LightGBM | 0.9972 | 0.9132
Ensemble Stack | 0.9962 | 1.0637

Table 5. Comparison of Model Performance for AQI Prediction in Hyderabad.

City | Algorithm | R² Score | RMSE
Hyderabad | XGBoost | 0.9985 | 1.6507
LightGBM | 0.9982 | 1.8373
Ensemble Stack | 0.9935 | 3.4767

The results indicate that the model performance varied across cities, with LightGBM consistently achieving lower RMSE and higher scores in cities like Amaravati and Chennai, while XGBoost excelled in Bengaluru and Hyderabad, demonstrating its ability to effectively capture feature relationships in these regions. The Ensemble Stack, however, underperformed, especially in Bengaluru and Hyderabad, likely due to noise introduced by combining models or suboptimal
weighting that may have increased prediction error. When models were compared based on RMSE and scores, both XGBoost and LightGBM displayed strong predictive capabilities, with LightGBM showing a slight advantage in most instances. The ensemble approach, however, yielded mixed results, often leading to increased RMSE, indicating lower generalizability than individual models.

Fig. 10. Visualization of Predictive Performance of LightGBM.

Fig. 10. Visualization of Predictive Performance of LightGBM.

Fig. 11. Visualization of Predictive Performance of XGBoost.

Fig. 11. Visualization of Predictive Performance of XGBoost.

Fig. 12. Visualization of Predictive Performance of Ensemble Model.

Fig. 12. Visualization of Predictive Performance of Ensemble Model.

For evaluating the predictive performance of the three models, scatter plots of predicted versus actual AQI values provided a clear visualization of each model's accuracy. In each scatter plot, a reference line ( ) represented ideal predictions, where the model outputs matched actual values accurately. The scatter plot of the LightGBM model, as illustrated in Fig. 10, shows a strong alignment with the reference line, demonstrating a tight clustering of points especially in the low to moderate AQI range ( ). It also indicates minimal deviation even at higher AQI levels, underscoring the model's high predictive accuracy across the entire AQI spectrum. XGBoost similarly maintained a reasonable degree of accuracy, with data points clustering near the equality line in the lower AQI range as shown in Fig. 11. However, it exhibited a slightly more scatter at elevated AQI levels. The Ensemble model resulted more variability at high AQI levels as illustrated in Fig. 12, where its predictions deviated more significantly from actual values, potentially due to noise or suboptimal weighting in the model integration.

These findings suggest that either XGBoost or LightGBM should be selected based on regional data characteristics, while the ensemble model requires further refinement to improve its utility in air quality forecasting applications. Further exploration into the underlying factors influencing model performance in each city could enhance our understanding of geographic-specific trends, providing a foundation for more tailored and robust predictive frameworks in future

Conclusion and Future Work

The original version of this paper is available on https://www.scientific.net/EI.14.99.pdf

Air pollution remains a pressing issue in India, significantly impacting human health and the environment. Despite efforts to mitigate this challenge, further research is essential to identify and implement sustainable, long-term solutions for pollution control. The convergence of COVID-19 with air pollution dynamics in urban settings has created an opportunity for valuable insights into
how environmental and health crises may interact, underscoring the need for comprehensive air quality management strategies. Integrating these insights can play a crucial role in reducing health risks and environmental damage, which is further validated by numerous global studies advocating for stringent policy actions to curb pollutants. Within this framework, advanced monitoring systems and machine learning technologies have become vital to Indian urban air quality management, providing predictive accuracy and actionable insights for policymakers. Future research can expand into developing spatiotemporal models for each primary air pollutant across various regions in India. By leveraging advanced geostatistical frameworks, these models could provide detailed analyses of pollution patterns at different spatial and temporal scales, improving the precision of air quality predictions across diverse zones. This approach would enable a targeted understanding of pollution sources and variations, supporting region-specific interventions. Furthermore, spatiotemporal models would enhance predictive capabilities by accounting for both location-based and time-based factors, enabling more adaptive and responsive pollution management strategies. Through such an integrative approach, modeling techniques could greatly contribute to a comprehensive solution for India's escalating air pollution crisis. These findings can guide policymakers in crafting informed, sustainable policies that effectively address pollution levels, fostering a healthier and more sustainable environment for future generations.

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Engineering Innovations, ISSN: 2813-1002, Vol. 14, pp 117-125

doi: 10.4028/p-A50unQ

© 2025 The Author(s). Published by Trans Tech Publications Ltd, Switzerland.

Submitted:
2024-08-29

Accepted:
2024-10-09

Online:
2025-04-04

An Improved Data Integrity Verification Model in Cloud Computing

The original version of this paper is available on https://www.scientific.net/EI.14.117.pdf

Nomsa L. Khumalo , Topside E. Mathonsi , Sunday O. Ojo

Department of Information Technology, Tshwane University of Technology, Pretoria, South Africa

Department of Information Technology, Durban University of Technology, Durban, South Africa

lebokhumalo22@gmail.com, MathonsiTE@tut.ac.za, sundayo1@dut.ac.za

Keywords: Cloud computing, Data integrity, Cryptographic accumulator provable data possession-merkle hash tree algorithm, Data verification.

Abstract

Cloud computing eliminates the need for expensive hardware and software expenditures by revolutionising access to computing resources through internet-based utility services. However, Data Integrity (DI) in this paradigm faces a variety of challenging issues, including complexity, security, privacy, control limits, fallibility of human beings, and financial limitations. The shortcomings of current DI solutions in terms of guaranteeing data verification, preventing replay attacks, and controlling computational overhead have led to an increasing need for access to cloud infrastructures by third-party verifiers. The suggested Cryptographic Accumulator Provable Data Possession with Merkle Hash Tree (CAPDP-MHT) scheme demonstrates significantly improved performance over Provable Data Possession (PDP) and Rivest Shamir Adleman (RSA) algorithms in various domains, as demonstrated by thorough simulation and MATLAB-based evaluation. In particular, CAPDP-MHT outperforms PDP and RSA with an average data verification success rate of , compared to their respective rates of and . Moreover, it identifies replay attacks in about 30 seconds, compared to 45 and 70 seconds for PDP and RSA, respectively. Furthermore, the computational overhead of CAPDP-MHT is about 27 seconds, while that of PDP and RSA is 45 and 60 seconds, respectively. Therefore, as compared to PDP and RSA-based systems, CAPDP-MHT not only exhibits exceptional computing efficiency but also outperforms in reliability.

Introduction

The original version of this paper is available on https://www.scientific.net/EI.14.117.pdf

A concept known as "cloud computing" makes it possible to distribute computer resources - like storage, processing power, and software - over the Internet [1]. Users don't need local hardware or physical infrastructure to access and use these resources on demand [2]. This technology, which offers scalability, flexibility, and cost-efficiency, has completely changed how people and corporations store, process, and manage data [3]. Data generation is accelerating in the modern era, outpacing the capacity of conventional devices like PCs and mobile phones [4]. Due to their limited resources, these devices find it difficult to manage the increasing volume of data that is stored locally [5]. Because of this, cloud storage has gained popularity as a result of its practically infinite storage space and adaptable pay-as-you-go pricing model [6]. However, outsourcing data to cloud storage raises a number of security issues. Three key components of data security are availability, confidentiality, and data integrity (DI). DI verification is essential to guaranteeing the reliability of data stored on remote servers [7]. Provable Data Possession (PDP) systems, for example, enable a client to confirm the accuracy of its data that is outsourced without having to get the complete dataset [8].

The topic of DI, which includes safeguarding data from unauthorised change or alteration and making sure it remains valid and consistent throughout its lifecycle, is the main subject of this study. When using cloud storage, consumers upload their data with the expectation that it won't be changed or removed. But there are possible hazards involved. To generate profit, cloud storage services may remove user data that is not commonly accessed in order to free up storage space and distribute it to other customers. Furthermore, user data deletions can happen by unintentionally, in which case the server would hide the information to preserve its good name.

According to a survey of the literature, researchers have previously put out a number of DI verification systems in an effort to solve this problem [9]. Scholars have predominantly directed their attention towards the domain of cloud computing. Although current algorithms have demonstrated efficacy in managing DI verification, they frequently encounter obstacles such replay assaults and elevated computational overhead [10]. These restrictions may have a major effect on the network's overall performance and quality of service. This work suggests integrating the Merkle Hash Tree (MHT) and Cryptographic Accumulator Provable Data Possession (CAPDP) techniques to address these issues. The CAPDP-MHT is the name of the planned scheme.

The goals of the CAPDP-MHT scheme's creation were to address the issues with replay attacks, DI verification, and computational overhead in cloud computing. In cloud computing, CAPDP is a useful tool for DI verification. It utilises an adapted cryptographic accumulator based on RSA to allow users to collect data block hash values [11]. Without having to retrieve the complete dataset, this accumulator makes data integrity verification easier. Furthermore, CAPDP provides some defence against replay attacks due to its reliance on an accumulator. The current accumulator state, which dynamically changes as new data is added or modified, makes it difficult for attackers to replay previous data and serves as a partial deterrent to replay attacks [12]. However, MHT is particularly good at handling the computational overhead involved in data integrity checking. Compared to rehashing the full dataset, it offers efficiency and lowers processing needs by just recalculating a tiny section of the tree when data changes [13]. The purpose of integrating these techniques into CAPDPMHT was to improve DI verification while lowering the chance of replay attacks and minimising computational complexity.

The subsequent sections of this paper are organized as follows: Section II offers an extensive examination of previous studies related to the DI verification schemes. In Section III, we introduce the proposed CAPDP-MHT algorithm. Section IV presents the simulation results obtained by applying the algorithm. Finally, in Section V, we outline future work and conclude the paper with remarks on the findings.

Related Works

The original version of this paper is available on https://www.scientific.net/EI.14.117.pdf

Significant attempts have been made in the last few years to improve DI in cloud computing. Researchers have created methods, algorithms, mathematical models, protocols, and other tools to confirm the accuracy of data kept in cloud storage

A simple deterministic DI check procedure based on homomorphic hash functions and RSA cryptographic public-key encryption was presented by Tsaloli et al. [14]. Their method did not support data dynamics, even though it produced adequate findings with an infinite number of verifications. In this work, we offer a modified RSA-based deterministic technique that concurrently supports data dynamics and enables users to check data integrity an infinite number of times.

A probabilistic DI approach for Provable Data Possession (PDP) was presented by Zhou [15]. Through this innovative approach, clients may now independently confirm the accuracy of data stored on untrusted servers without having to retrieve the complete dataset. Instead of reading the entire file, a random sampling of data blocks was used in this approach. It was found through experimentation that disc input/output (I/O) operations had a greater impact on PDP performance than cryptographic calculations. Performance showed decreasing returns as file size rose.

Our study, on the other hand, uses a proved data-possession technique to guarantee the integrity of data that is outsourced. Rather than depending on a subset for verification, our deterministic technique seeks to check the integrity of every data block in a dataset.

An integrity-checking approach based on message authentication codes (MAC) was presented by Yi et al. [16] and does not require a third party's involvement as a requirement. Thanks to the method developed by these researchers, stored data can be validated locally with an exceptionally high degree of accuracy. Users send a verification tag - such as block numbers - when verification is required, and the cloud server verifies it. This technique resists replay and man-in-the-middle attacks and efficiently verifies the integrity of the data. The MAC approach does, however, have the disadvantage of storing redundant data, which raises the overhead costs associated with computation and storage.

In this work, we propose a substitute verification technique that provides protection against replay assaults as well as ensuring the integrity of data saved in the cloud. In addition, we use an adapted RSA-based accumulator to confirm the accuracy of data that is outsourced, which lowers the overhead associated with computation and storage.

A symmetric cryptographic approach was presented by Ferretti et al. [17] in order to validate data DI in cloud databases. These researchers' method finds unapproved changes to outsourced data by using many bloom filters. Although this system can identify replay attacks, it is unable to stop them.

Cryptographic Accumulator Provable Data Possession - Merkle Hash Tree (CAPDP-MHT) Scheme

The original version of this paper is available on https://www.scientific.net/EI.14.117.pdf

Cryptographic-Accumulator Provable Data Possession (CAPDP)-Merkle hash tree (MHT) (CAPDP-MHT) scheme was designed by integrating CAPDP and MHT in order to improve data integrity in cloud computing.

An RSA-based cryptographic accumulator is used by CAPDP, a useful tool for cloud computing DI verification, to streamline DI testing. It lessens the requirement to retrieve the complete dataset by enabling users to collect hash values of data blocks. Because the accumulator state dynamically alters in response to updates in the data, CAPDP also provides security against replay attacks. However, MHT excels at lowering computational overhead by improving efficiency by just recalculating a small portion of the tree when data changes. By using these algorithms, CAPDP-MHT improves DI verification while reducing computing needs and replay assaults.

Three important players are shown in Fig. 1's cloud computing architecture: the data owner (DO), the third-party auditor (TPA), and the data storage service (DSS), which is made up of four entities. Cloud service providers (CSPs) provide storage services with great computational capabilities, and the DO has a large amount of data for cloud storage. TPAs ensure security and integrity by managing and supervising the data that DOs outsource. The outsourced data is accessible to authorised applications (AAs). TPA protects data, DO uploads data to the cloud, CSP keeps resources updated, and AAs use a range of cloud-based apps. In cloud computing, this structure makes data storage, security, and accessibility easier.

Fig. 1. Cloud computing architecture.

Fig. 1. Cloud computing architecture.

Cryptographic-Accumulator Provable Data Possession. The CAPDP makes DI checks easier. It lessens the requirement to retrieve the complete dataset by enabling users to collect hash values of data blocks [11]. Since the accumulator state dynamically changes in response to updates in the data, CAPDP also provides security against replay attacks.

Verification remains unaffected by the volume of data blocks; and can be carried out on lowpower devices. Additionally, the scheme functions as a safeguard against a range of attacks, including tag forgery, data deletion, replacement, and leakage, while also detecting replay attacks.

Equation 1: Generating two symmetrical numbers
Step2: generate_two_numbers
In this equation, the algorithm generates two symmetrical numbers, w, and z. The specific method or process used to generate these numbers is not mentioned in the algorithm and would depend on the implementation.

Equation 2: Calculating m Step 3:
This equation calculates the value of m by multiplying the two symmetrical numbers, w , and z , obtained in Equation 1. The resulting value, m , will be used in subsequent steps of the algorithm.

Equation 3: Calculating y Step 4:
In this equation, the algorithm calculates the value of y . The equation uses the previously defined variable g (which is not explicitly defined in the algorithm) and subtracts 1 from this variable. Similarly, the algorithm subtracts 1 from the symmetrical number z . It then multiplies these two values to obtain . The resulting value of will be used in Step 5.

Equation 4: Selecting an integer approximately prime to y . Step 5: select_approximately_prime_integer(y)

In this equation, the algorithm selects an integer that is approximately prime to the value of obtained in Equation 3. The specific method for selecting such an integer is not provided in the algorithm. The selected value of will be used in subsequent steps.

Equation 5: Calculating the public and private keys Step 7: Public Key , a Step 8: Private Key

In these equations, the algorithm defines both the public and private keys. The public key consists of the value of g (which is not explicitly defined in the algorithm) and another variable a. The private key consists of the value of c and the same variable a. The specific values assigned to a, g, and c are not specified in the algorithm and would depend on the implementation.

Key Generation Algorithm - CAPDP uses two types of keys: public keys and private keys. Some of the steps for key generation are as follows:

Algorithm 1: CAPDP Key Generation

The original version of this paper is available on https://www.scientific.net/EI.14.117.pdf

Input: Generate two symmetrical numbers
Output: Public & private keys
Step1: Begin the process:
Step2: Generate two numbers w & z
Step3: Calculate
Step4: Calculate
Step5: Select an integer approximately prime to it b
Step6: Calculate
Step7: The public key is
Step8: The private key is
End procedure
2. Encryption Algorithm - An encryption algorithm is used to turn data into a cipher text(unreadable) format only authorized parties can access. Some of the steps for encryption are as follows:

Algorithm 2: CAPDP Encryption

The original version of this paper is available on https://www.scientific.net/EI.14.117.pdf

Input: public key, simple text or plaintext & modulus (absolute values)
Output: cipher text or unreadable text
Step 1: Begin procedure
Step 2: Generate a public key (g, a)
Step 3: Assign a positive integer to the plaintext message
Step 4: Calculate the cipher text
Step 5: Send the cipher text or encrypted data
End process
3. Decryption Algorithm - Decryption is the process of converting cipher text or encrypted data back to its original form or readable data. Some of the steps for decryption are as follows:

Algorithm 3: CAPDP Decryption

The original version of this paper is available on https://www.scientific.net/EI.14.117.pdf

Input: Public key, cipher text & modulus (absolute values)

Output: Plaintext or readable data
Step 1: Initiate the process.
Step 2: The user initiates a request to the service provider, asking for user authentication and the provision of encrypted data, denoted as 'd'.

Step3: The user performs decryption through calculation, resulting in the determination of

Step 4: Return the plaintext from an integer representation of x .
End process
Merkle Hash Tree. MHT is a useful tool for data synchronisation and verification, allowing for quick and easy assurance that a group of pieces is still intact [18]. Implementing it reduces server processing time significantly. By merely recalculating a small portion of the tree when data changes, MHT excels at lowering computational overhead and improving performance.

Based on the provided description of the Merkle hash tree (MHT), here are five equations that represent the steps involved:

Equation 6: Leaf node hashing: H1 = Hash(file_block_1) H2 = Hash(file_block_2)
Equation 7: Combining and hashing intermediate nodes:
Equation 8: Hashing and combining additional file blocks: H4 Hash(file_block_3) H5 Hash(file_block_4) H6 Hash(H4 + H5)

Equation 9: Rehashing and obtaining the root node: H7 = Hash(H3 + H6)
Equation 10: Final expression of the root: Root
These equations illustrate the process of generating an MHT for data verification and synchronization. The specific hash function used may vary based on the chosen cryptographic method, and the example provided is a simplified representation of the steps involved.

CAPDP-MHT Flowchart. The CAPDP-MHT flowchart depicted in Fig. 2 can be employed collaboratively to transmit a text message from Workplace 1 to Workplace 2.

Fig. 2. CAPDP-MHT flowchart.

Fig. 2. CAPDP-MHT flowchart.

Simulation Results

The original version of this paper is available on https://www.scientific.net/EI.14.117.pdf

The simulation studies carried out to assess how well the CAPDP method ensures DI and security are presented in this section.

Data-verification experiment:

Dataset sizes: , and 10 GB
Verification process: Perform DI verification using the CAPDP-MHT scheme on each dataset.
Performance metrics:
Verification time: Measure the average time taken for the CAPDP-MHT scheme to verify the integrity of the datasets.

Resource utilization: Evaluate CPU usage and memory consumption during the verification process.
2. Replay attack experiment:

Types of replay attacks: Timestamp replay attacks and data-modification replay attacks.
Varying attack intensity: Introduce replay attacks at different frequencies, e.g., and of the dataset.

Performance metrics:
Detection rate: Calculate the percentage of successfully detected replay attacks of the total number of attack instances.

False-positive rate: Measure the rate of false-positive detections, indicating legitimate data being wrongly identified as replay attacks.
3. Computational overhead experiment:

Varying dataset complexity: Introduce different levels of data modification or corruption within the dataset.

Varying system configurations: Adjust the number of parallel processing threads (2 and 4 threads) and available memory ( 8 GB and 16GB).

Performance metrics:
Processing time: Measure the time taken for the CAPDP-MHT scheme to perform DI verification for each dataset complexity and system configuration.

Memory usage: Evaluate the memory consumption during the verification process.
CPU utilization: Measure the percentage of CPU usage during the verification process.

Table 1. Simulation Parameters.

Experiment
Parameter
Value

Data Verification Experiment
Dataset sizes
Dataset 1: 1GB
Dataset 2: 5GB
Dataset 3: 10GB

Verification time
Dataset 1: 25 seconds
Dataset 2: 120 seconds
Dataset 3: 250 seconds

Resource utilization
CPU usage: 80%
Memory consumption: 6GB

Replay Attack Experiment
Types of replay attack
Timestamp replay attacks, Data-modification replay attacks.

Varying attack intensity
Replay attack frequency 1: 10% of the dataset
Replay attack frequency 2: 20% of the dataset

Detection rate
Replay attack frequency 1: 95%
Replay attack frequency 2: 85%

False-positive rate
Replay attack frequency 1: 2%
Replay attack frequency 2: 5%

Computational Overheads Experiment
Varying dataset complexity
Dataset complexity 1: 10 seconds
Dataset complexity 2: 45 seconds
Dataset complexity 3: 80 seconds

Varying system configurations
Parallel processing threads: 2 Threads
Parallel processing threads: 4 Threads
Available memory: 8GB
Available memory: 16GB

Processing time
Dataset complexity 1: 10 seconds
Dataset complexity 2: 45 seconds
Dataset complexity 3: 80 seconds

Memory usage
Dataset complexity 1: 4gb
Dataset complexity 2: 8gb
Dataset complexity 3: 12gb

CPU utilization
Dataset complexity 1: 60%
Dataset complexity 2: 80%
Dataset complexity 3: 90%

Table 1 provides an overview of the parameters and their corresponding values for each simulation experiment.

Data Verification. The average data-verification times for three different algorithms - with a particular emphasis on the number of challenged blocks ranging from 100 to 400 - are shown in Figure 3. Two-point multiplication operations have been added into this data-verification method. Nonetheless, the time variance is negligible - less than 1 ms - when compared to the PDP and RSA systems. This makes it essentially meaningless in situations involving actual data verification. As a result, the suggested strategy both improves security and keeps processing costs within an acceptable range.

The suggested CAPDP-MHT, the PDP model, and the RSA model show rates of , and , respectively, with regard to data-verification averages.

Fig. 3. Data verification.

Fig. 3. Data verification.

Replay Attack. The examination of the three systems was part of the assessment, which also examined the average replay-attack incidence that occurs during cloud computing data access. We looked at the time required for attack detection in the PDP, RSA, and the proposed CAPDP-MHT scheme, as shown in Fig. 4. It is important to note that replay-attack detection takes longer with all systems, including the suggested and current approaches (PDP and RSA). The suggested CAPDPMHT method, on the other hand, detected replay assaults in 30 seconds, whereas the PDP and RSA schemes required 45 and 70 seconds, respectively.

Fig. 4. Replay attack.

Fig. 4. Replay attack.

Computational Overhead. This study compares the average computational overhead of the PDP and RSA algorithms with the suggested CAPDP-MHT scheme, which is based on 10 simulations. Upon closer inspection, Fig. 5 clearly illustrates how much the suggested CAPDP-MHT method reduces the average computational cost in comparison to the other techniques. This decrease has been achieved by quickly and effectively certifying the integrity and unaltered state of a set of items using the MHT algorithm. Consequently, the MHT algorithm significantly reduces the amount of time needed for the data-verification procedure. Specifically, the average computational overhead for the CAPDP-MHT method was about 27 seconds, compared to 45 and 60 seconds for the PDP and RSA systems, respectively.

Fig. 5. Computational overhead.

Fig. 5. Computational overhead.

Conclusion and Future Work

The original version of this paper is available on https://www.scientific.net/EI.14.117.pdf

The goal of the CAPDP-MHT technique, which was presented in this paper, is to reduce computational overhead, lower the risk of replay assaults, and verify DI. We combine the MHT and CAPDP schemes to create the CAPDP-MHT scheme. The CAPDP-MHT is the name of the planned scheme. In order to ensure replay attack and computational overhead in cloud computing, we incorporated the CAPDP scheme and MHT scheme to secure DI verification. The reason CAPDP was chosen was its resistance to replay assaults. The MHT scheme was selected to serve as a useful tool for data verification and synchronisation, enabling quick and easy confirmation of the integrity and unchanged status of a group of elements. This is accomplished by continuously changing the accumulator state in response to data modifications, making it less vulnerable to such security breaches. The adoption of it considerably cuts down on server processing time. One notable feature of MHT is its ability to reduce computing overhead by just recalculating a portion of the tree when data changes, which improves overall performance. We verified the efficacy of the suggested CAPDP-MHT with comprehensive MATLAB computer simulations. The outcomes showed that it could successfully prevent replay attacks and minimise computing complexity while securing DO verification.

Future work employing machine learning to assess data accuracy in data integration should also examine a number of other crucial aspects. These variables include things like fault tolerance, resource efficiency, and scalability.

Acknowledgment

The original version of this paper is available on https://www.scientific.net/EI.14.117.pdf

The Tshwane University of Technology is acknowledged by the authors for its financial help in carrying out this research. They also attest that there are no conflicts of interest associated with this paper's publishing

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The original version of this paper is available on https://www.scientific.net/EI.14.117.pdf

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This ePub file was created using machine-generated methods and automated software to convert the source text into an ePub format and may contain formatting inconsistencies from the original version. Formatting may vary depending on the device used.

The original version of this eBook is available on https://www.scientific.net/book/engineering-innovations-vol-14/978-3-0364-1777-6

Engineering Innovations, ISSN: 2813-1002, Vol. 14, pp 127-138

doi: 10.4028/p-fiJ4gb

© 2025 The Author(s). Published by Trans Tech Publications Ltd, Switzerland.

Submitted:
2024-07-27

Accepted:
2024-09-19

Online:
2025-04-04

PRISMA Approach for Assessing Fingerprint Classification Models Based on Artificial Super-Intelligence Techniques

The original version of this paper is available on https://www.scientific.net/EI.14.127.pdf

Alrasheed Mustafa , Richard Millham , Hongji Yang

ICT & Society Research Group, Durban University of Technology, Durban, South Africa

Hongji Yang, School of Computing & Mathematical Sciences, University of Leicester, Leicester, UK

alrasheedmustafa@aol.com, richardm1@dut.ac.za, chy124@leicester.ac.za

Keywords: Fingerprint-Classification, Artificial-Super-Intelligence (ASI), Systematic Review, Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA).

Abstract

The aim of this research paper focused on using PRISMA to reveal most artificial intelligence techniques that were used for fingerprint classification. Biometric technology such as fingerprints plays a key role in authenticating and identifying people's identities. Therefore, with the increasing number of population and the usage of biometrics for authentication, fingerprint classification systems are becoming important and indispensable for recognizing and authenticating individuals. Therefore, Artificial Super-Intelligence (ASI) techniques such as bioinspired algorithm, deep learning and machine learning were used to improve fingerprint classification accuracy. The proposed method aimed to assess fingerprint classification models based on ASI. The researchers employed PRISMA approach, which is based on systematic analysis and is used to select, evaluate and analyze journals. Although IEEEXplore and Web of Science were utilized to extract journal articles from 2019 to 2023 . As a result, 1350 articles were found in both databases. Furthermore, a total of 35 publications were assessed to determine their eligibility and 19 articles were eliminated with reasons and 16 matched the requirements for a meta-analysis. Our findings demonstrate and highlight the need for developing a new approach to improve fingerprint classification accuracy.

Introduction

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Fingerprint biometrics is a method for verifying and authenticating individuals identities [1]. For instance, biometric recognition technologies like fingerprints, palmprints, iris and face are used to identify and authenticate individuals [2]. Generally, fingerprint is the most used biometric identification method because of its permanence, singularity, ergonomics, throughput, affordability and lifetime usability. Genuinely, even identical twins have different fingerprints since each person has unique fingerprint characteristics that cannot be changed [3]. Fingerprints play a vital role among other biometric features or traits. Therefore, fingerprints have been used for recognizing individuals in several instances such as identity management, border access control and forensic investigation [4]. Fingerprint biometrics offers a greater authentication method than the traditional method of person recognition which is based on identification documents, pins and password [1]. Fingerprint biometric is an important system that detects a person based on a feature vector that is derived from a particular physiological or behavioral attribute that the individual possesses [5]. Collectively, fingerprint features are useful, when identifying fingerprint classes based on their global pattern of ridges and valleys which relies on fingerprint classification [6].

Fingerprint classification has evolved since the 1890s due to the uniqueness of fingerprint characteristics and the authentication of individual identities [7]. As a fingerprint classification method, Henry's classification approach is the most widely used in fingerprint categorization technique [8]. The Henry's classification system, then developed into eight classifications of prints particularly plain arch, right loop, tented arch, plain whorl, left loop, double loop whorl, central-pocket whorl and accidental whorl [8]. The classification of fingerprint images is shaped by it is pattern and fingerprint characterization [9]. Historically, fingerprints were similar to a picture which contains sufficient qualitative information but lacks any intrinsic traits that would allow it to be classified. Therefore, Bertillon found the solution by reducing the body to a set of numbers [7, 10].

This means that information about individuals can be retrieved from a fingerprint image. Besides, some studies did not propose that these ridges can be used for identification but the first attempt was made to categorize the pattern types, grouping them into nine categories. This today would correspond to the arch, tented arch, radial loop, ulnar loop, double loop, central loop and plain whorl such as circular and spiral and accidental [7,11].

Additionally, the hybridization of fingerprint classification models has served as the foundation for all fingerprint classification methods that were used globally between 1890 and 1930 [7, 12]. The heuristic approach of fingerprint classification was proposed in 1900. One method for automated fingerprint classification is to codify the knowledge of human experts using a system of heuristic rules such as a combination of singularity and ridge features. Moreover, based on heuristics fingerprint classification has developed a six-class of fingerprint classification scheme. The major drawback of the system was its only reliance on singularity features for fingerprint classification which resulted in an inaccurate distinction of core and delta points [13]. Research has continued to explore the applicability and usability of neural networks in fingerprint classification [14]. The digitalization of fingerprint classification continues to add value to fingerprint classification development such as by using neural networks [14]. For instance, neural network approach was used in fingerprint classification and 100 fingerprints were classified which consisted of a total of 500 fingerprints. The findings indicated that for 500 unknown samples was an average of classification rate for the first candidate and classification rate for the second candidate [14].

Fingerprint classification continues to have a variety of approaches that are proposed either to improve performance or accuracy [15, 16]. In this regard, fingerprint classification based on learned features using genetic programming that learns to discover composite operators and features [17]. Moreover, Bayesian classifier was utilized to classify the fingerprints from NIST-4 dataset which has 4000 fingerprints. Therefore, fingerprint was classified into 4 and 5 classes and the experimental results indicated that the accuracy rate for the 4 and 5 class classifications were and respectively [17]. On the other hand, Poincare Index method is proposed for the classification of fingerprints, the results illustrated that the Poincare method is limited to only providing the type and position of minutiae. Therefore, the Adaboost learning algorithm was utilized for the classification of fingerprints and the results show that Adaboost methods can provide the type and positions of minutiae but also this approach can compute the directions and certainty of fingerprint minutiae [18]. The main drawback of the Adaboost was the misclassification which was caused by heavy noise in the poor quality of fingerprint and also when a pair of core and delta are too close to detect. Further, the experiment findings show that using NIST-4 fingerprint dataset, the Adaboost method can automatically select the discriminatory features for fingerprint classes [18].

Related Work

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Artificial Super-Intelligence (ASI) Utilized in Fingerprint Classification. The term Artificial Super-Intelligence (ASI) is used to describe the categories of AI techniques such as bioinspired algorithm, deep learning, neural networks and machine learning approaches that are used to improve fingerprint classification accuracy (see Fig.1). The term artificial super-intelligence, first used in 1970 which has been used to describe computer skills that surpass or even beyond the capacities of the human intellect. The goal of artificial super-intelligence is to enhance machine cognition. Alternatively, the benefits of developing artificial super-intelligence include increased efficiency and automation, improved problem-solving and less chance of error. Artificial super-intelligence is also recognized as super AI. For instance, learning algorithms of bioinspired optimization, machine learning, deep learning and neural networks have become artificial super-intelligence in improving fingerprint classification accuracy. Artificial super-intelligence is meant to ensure that the AI is mature and to accelerate the development and advancement of fingerprint classification [19]. In that sense, Artificial super-intelligence revolutionizes development and seeks to establish an optimization function [20]. Overall, ASI would be powerful and effective at achieving outcomes, faster tasks, mimicking human intelligence, and also becoming fully self-aware [20,21].

Fig. 1. Artificial super-intelligence approach used on improving fingerprint classification accuracy.

Fig. 1. Artificial super-intelligence approach used on improving fingerprint classification accuracy.

Fingerprint Classification Based on Artificial Super-Intelligence. Deep learning methods are deep neural networks that have been known as one of the most potent technologies during the past few decades. Deeper hidden layers started to outperform traditional approaches in a variety of applications including fingerprint classification. CNNs are among the most often used deep neural networks in fingerprint classification [22]. This accomplishment has inspired both researchers and developers to use larger models to tackle challenging problems that could not be solved with traditional Artificial Neural Networks (ANNs) [22]. Furthermore, ANN is similar to CNN because it's made up of layers and artificial neurons with weight and biases. ANN is limited in its ability to handle large datasets, in contrast to CNN which can process large image datasets and enable the collection of more complex and abstract features in images [23]. Another crucial component of CNN is getting abstract features when input propagates toward the deeper layers [22]. The main difference between ANN and CNN is that CNNs are tuned to work with images like 2D and 3D. CNNs are a subset of deep learning that are very adept at classifying images. A typical architecture of CNN consists of convolution layer, pooling layer and fully connected layer.

A lightweight CNN model was developed for fingerprint classification as a solution to reduce computational and network complexity while maintaining impressive accuracy. Lightweight CNN models with fewer parameters can therefore train more quickly and at a lower cost, allowing them to be used in infrastructure with insufficient computer resources [24]. Similarly, the experiment results showed that the lightweight CNN model structure had better accuracy and outperformed the non-neural network classifiers like the random forest, K-Nearest-Neighbor (KNN) and linear SVM [24]. In addition, the truncation of the model is to reduce the complexity and quantity of parameters used in fingerprint image training without impacting the image performance [25]. The high processing complexity of fingerprint classification systems is one of their main drawbacks due to limitations. Deep learning algorithm is effective and reliable for the classification of fingerprints. For instance, Fukunaga-Koontz Transform was introduced for automatically determining the architecture of the CNN model which was adaptive to fingerprint classification. The DeepFKTNet model was hyperparameter tuned using the Optuna optimization algorithm which has been tested on the optimizers like learning rate, patch size and activation function. Therefore, since CNN models contain a large number of parameters, the FKT approach was built as a low-cost and high-speed CNN model for the classification of fingerprints [26].

The CNN architectures particularly AlexNet, GoogLeNet and ResNet have been used in fingerprint image classification. These models compare favorably in terms of training performance for fingerprint image classification and have a faster rate of convergence. From a computational standpoint, AlexNet needs the least amount of training time compared to GoogLeNet and ResNet. In utilizing two different databases PolyU and NIST, the three used CNNs performed well with average precision values higher than on the PolyU database. Moreover, the disadvantage of GoogLeNet and ResNet is that data processing takes longer and some of their layers were frozen to improve the processing speed and performance [27]. In GoogLeNet and ResNet if the training setting is adjusted with a few parameters it can train faster with less computational cost [27]. Although the weight-sharing feature of CNNs is the primary factor to take into account because it lowers the number of trainable network parameters which in turn aids the network in enhancing generalization
and preventing overfitting [28]. Nevertheless, AlexNet's architecture is more straightforward which reduces the training time and it does not limit the output image data [27]. AlexNet was coined by Alex Krizhevsky and also AlexNet presents a deeper neural structure, composed of a higher number of pooling and convolutional layers [27, 29].

The application of a hybrid approach using swarm intelligence algorithm in fingerprint classification has been explored. There are a few studies that have implemented bioinspired algorithm or metaheuristic approaches in fingerprint classification [30]. For instance, hybrid approach based on a combination of Particle Swarm Optimization (PSO) and Support Vector Machine (SVM) was implemented to improve fingerprint classification accuracy [31]. The proposed methodology is divided into three stages: preprocessing, feature extraction and classification using PSO-SVM model. In the preprocessing stage, fingerprint images were converted into binary images which is called binarization to improve the intensity of the fingerprint images for the preparation of edge detection and feature extraction phase [31]. The PSO-SVM fingerprint classification model is tested in the experiments using the CASIA V5 fingerprint dataset for the assessment of the fingerprint classification model. The experimental findings demonstrated that the PSO-SVM classification model outperforms the traditional SVM method in terms of accuracy, sensitivity and precision [31].

Mishra (2019) introduced fingerprint classification using three different hybrid techniques consisting of Biogeography Based Optimizer (BBO), PSO and Genetic Algorithm (GA) combined with a Functional Link Artificial Neural Network (FLANN). The classification approach involves adaptation and tuning of classifier parameters for better classification accuracy [32]. The dataset utilized is a sample of 50 fingerprint images of 10 students from the Silicon Institute of Technology. The extracted fingerprint image features of 50 students were set in Excel sheet and used for training and testing of the network [32]. The BBO, PSO, GA are optimizers to update the weight parameters of FLANN classifier for testing classification accuracy. Furthermore, the tuning of FLANN with hybrid techniques is done randomly and manually by hit and trial to improve fingerprint classification accuracy. The implementation and experimentation of PSO-FLANN, GA-FLANN and BBO-FLANN are designed and tested for fingerprint classification accuracy. In comparison to all, the findings showed that the PSO-FLANN technique exhibits superior performance with a high rate of accuracy. The main drawback of PSO-FLANN is the execution time was considered high [32].

Methodology

The original version of this paper is available on https://www.scientific.net/EI.14.127.pdf

In this section, the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) have been utilized to provide evidence on fingerprint classification models based on ASI approaches. PRISMA includes the use of meta-analysis which integrate study findings from several PRISMA checklists and aggregate the outcomes of multiple research publications through a systematic review procedure. The PRISMA checklist is a crucial resource for reporting the results of a systematic review and meta-analysis for providing a comprehensive report on the literature review and the progress made in fingerprint classification research and development. In this regard, using the IEEE and Web of Science databases which include scholarly literature from many domains, a thorough analysis and methodical literature search were carried out. Similarly, the screening procedure is graphically summarized in the PRISMA flow diagram as shown in Fig. 2. Moreover, PRISMA approach was used for the study selection and the keywords are (Fingerprint Classification AND Deep Learning AND Machine Learning AND Neural Network AND Bioinspired OR Metaheuristic Algorithm). The search for the articles was acquired between the years 2019 and 2023. This led to the publication of research that solely used fingerprint classification based on ASI approaches for analysis. Although in terms of quality standards to assess the reliability of the selected journal articles. As a result, the articles that were taken from the databases are relevant to this study and sufficient. Initially, 1350 research papers were found from both IEEE-Xplore and Web of Science databases, then screened accordingly. Moreover, 1187 articles were excluded, and 16 research articles were eligible to be included in the meta-analysis with the following attributes: article title, publication year, author name, classifier, dataset and overall accuracy as described in Table 1.

Fig. 2. PRISMA flowchart elucidating the selection of studies.

Fig. 2. PRISMA flowchart elucidating the selection of studies.

Results and Discussion

The original version of this paper is available on https://www.scientific.net/EI.14.127.pdf

This section presents the results of assessing fingerprint classification based on artificial super-intelligence techniques, which aim to improve fingerprint classification accuracy. In this regard, a total of 16 articles and conference papers were identified for the study context as elucidated in section 3 and Fig. 2. Notably, journal articles that were published in scientific journals served as the main information sources as indicated in Table 1. PRISMA approach was carried out to analyze the checklist indicated in Table 1 such as authors, year of publications, classifier, dataset, sample and accuracy. Additionally, it has been shown that deep learning models perform astonishingly well in tasks involving fingerprint categorization. For instance, the classifier column elucidates deep learning techniques such as AlexNet, RestNet50, GoogLeNet, DenseNet121, LeNet and CaffeNet. Although machine learning techniques are included such as random forest, few-shot, SVM and Naïve Bayes. KNN is a non-parametric learning classifier that has been utilized in fingerprint classification as well. The dataset, sample and accuracy columns show how well fingerprint classification performs when the classifier is used to find the most optimum solution. Basically, the fingerprint dataset consists of digital images that were captured for evaluation, comparison and usefulness in the fingerprint classification process using an algorithm to show improved accuracy and performance. The distinct dataset comprises unique fingerprint features that are filtered and then stored as a mathematical representation and encrypted as biometric information. As shown in Table 1, the fingerprint image, dataset and fingerprint sample are all binary codes that are utilized for verification and authentication of fingerprint. The accuracy column shows the percentage yield during the fingerprint classification process for each research paper that were selected for meta-analysis. Although some authors used both datasets and samples. Moreover, each author has unique results that have been obtained for improving fingerprint classification accuracy. In this regard, we've used the most important PRISMA checklists that are related to the research paper context as indicated in Table 1.

Table 1. Describe the classification technique & accuracy.

Authors
Year
Classifier
Dataset
Sample
Accuracy

[33]
2023
AlexNet CNN
N/A
500
96.7%

[34]
2023
Random Forest
FVC2002
N/A
96.88%

[25]
2023
ResNet50 CNN
NIST SD4
800
93.3%

[35]
2022
CNN
SOCOfng
6000
99.98%

[36]
2021
Few-shot
NIST-4
600
92.34%

[37]
2021
Naïve Bayes-SVM
AlexNet-Multiclass SVM
AlexNet-Multiclass NB
PolyU 3D
N/A
98.6%
97.5%
90.2%

[36]
2021
AlexNet CNN and
Calibration algorithm
HONoPert
Default
VQAndPert
NIST F
NIST S
4000
98.4%
98.35
96.75
88.48
84.85

[38]
2021
Decision trees,
Linear Discriminant
Analysis
Naïve Bayes
SVM
k-NN
Ensemble classifiers
1. DB1-2000
2. DB2-2000
3. DB3-2000
4. DB4-2000
960
1/ (76.3%/ 90%/
90%/ 93.8%/
93.8%/ 95%).
2/ (72.5%/ 95%/
82.5%/ 92.5%/ 90
%/ 96.3%).
3/ ( 63.7%/ 95%/
92.5%/ 96.3%/
92.5%/ 98.8%).
4/ (76.3%/ 92.5%/
85%/ 93.8%/
82.5%/ 96.3% ).

[27]
2021
AlexNet CNN
GoogLeNet CNN
ResNet CNN
NIST &
PolyU
(Eight-class)
7800
93.75/ 92.07/ 92.71
& 99.51 / 99.58/
99.31

[39]
2020
One-Versus-All Twin-
SVM / Binary Tree
Quantum PSO /
Twin-SVM
Binary Tree SVM
NIST-4
2000
87.13%/ 93.83%/
90.35%

[40]
2020
Extreme Learning Machine
based on CaffeNet CNN
HQNoPert/
Default/
VQAndPert
30000
99%/ 98%/ 96%

[41]
2020
DenseNet 121 CNN
NIST-4
4000
97.7%

[24]
2020
CNN
NIST SD4
20000
93%

[42]
2020
Alexnet CNN
LeNet CNN
CaffeNet CNN
NIST:
Six-class
100000
57.84%/ 17.03%/
91.63%

[43]
2019
GoogLeNet CNN
NIST-4:
five-class and
four-class
4000
94.7% and 96.2%

[44]
2019
Random Forest & SVM
NIST-DB4
4000
96.75% & 95.5%

Fig. 3 indicates the total number of journal articles that were found in the databases. There are nine journal articles from IEEE and six from Web of Science. These journal articles pertain to the study of fingerprint classification based on an artificial super-intelligence approach.

Fig. 3. The graph represents journal articles found in databases.

Fig. 3. The graph represents journal articles found in databases.

Fig. 4 displays the total number of selected published journal articles per year. Furthermore, there were 2 journal articles published in 2019, and 5 articles were published for each year in 2020 and 2021. Similarly, in 2022, one journal paper was published and three publications were published in 2023.

Fig. 4. Total number of articles published between 2019 and 2023.

Fig. 4. Total number of articles published between 2019 and 2023.

The percentage of published articles by country and the total number of articles are displayed in Fig 5 and 6.

Fig. 5. Total number of published articles per country.

Fig. 5. Total number of published articles per country.

Fig. 6. Percentage of published articles per country.

Fig. 6. Percentage of published articles per country.

Fig. 7 shows the number of published articles for each country. For instance, 4 describes the number of articles that is published, and yellow represents the country which is India and so forth for others.

Fig. 7. The number of publications for each country.

Fig. 7. The number of publications for each country.

Figs. 8 and 9 indicates the overall number of AI approaches undertaken in each study and the percentage of each used approach.

Fig. 8. Total number of approaches used in various studies.

Fig. 8. Total number of approaches used in various studies.

Fig. 9. Percentage of each approach utilized.

Fig. 9. Percentage of each approach utilized.

In summary, a comprehensive analysis of 16 published articles were carried out on fingerprint classification based on machine learning and deep learning for improving fingerprint classification accuracy. In general, the studies shown various classifiers were utilized as mentioned in Table 1. Although in Fig 5 and 6 indicated that India is the most country that has published journal articles in fingerprint classification and followed by China and Indonesia. These significant differences in national research focus could be attributed, in part to India having the largest national biometric identification system in the world [45]. India uses biometrics to identify and verify individuals because of its large population of 1.2 billion citizens. Therefore, Indian institutions are working to enhance their national biometric identification system through research and the application of bioinspired algorithm [45]. Nevertheless, in 2023, we conducted a systematic review on bioinspired algorithm based on fingerprint classification models. Furthermore, we found that hybrid methods that combined bioinspired optimization and deep learning are not utilized to improve fingerprint classification accuracy [30]. Lastly, there are a few studies on fingerprint classification combined with a bioinspired algorithm and classical model and the results indicated that the combined model outperforms the standard classical technique [31].

Conclusion

The original version of this paper is available on https://www.scientific.net/EI.14.127.pdf

In this research paper, we presented state-of-the-art PRISMA analysis for assessing fingerprint classification models based on artificial super intelligence approaches. The findings shows that combined approaches like deep learning and bioinspired optimization haven't been used for improving fingerprint classification accuracy. The hybrid approach of artificial super intelligence to fingerprint classification promises to be the future endeavor. For this reason, future research will therefore focus on developing a fingerprint classification model based on the hyper-parameterization of a traditional method using a metaheuristic algorithm.

Acknowledgement

The original version of this paper is available on https://www.scientific.net/EI.14.127.pdf

This research work is based on research wholly/in part by the National Research Foundation of South Africa (Grant Numbers 151178).

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The original version of this eBook is available on https://www.scientific.net/book/engineering-innovations-vol-14/978-3-0364-1777-6

Engineering Innovations, ISSN: 2813-1002, Vol. 14, pp 139-146

doi: 10.4028/p-YUq30L

© 2025 The Author(s). Published by Trans Tech Publications Ltd, Switzerland.

Submitted:
2024-07-26

Accepted:
2024-09-19

Online:
2025-04-04

Personalized Adaptive Learning Management System Based on Bioinspired Algorithm: A Systematic Review

The original version of this paper is available on https://www.scientific.net/EI.14.139.pdf

Feziwe Lindiwe Yvonne Khomo , Richard Millham , Alrasheed Mustafa

ICT & Society Research Group, Durban University of Technology, Durban, South Africa

Keywords: Adaptive Learning, Learning Management System, Bioinspired Optimization Algorithm, Systematic Literature Review.

Abstract

It is possible to address learning challenges in a way that is unique to the needs and preferences of students through adaptive learning environments. Using these platforms, learning content can be personalized to reflect a user's interest, past knowledge, present abilities, and strengths and limitations. Conversely, the efficiency of adaptive learning systems depends on the techniques adopted to classify and present the content according to students' needs and preferences. Artificial Intelligence (AI) techniques have recently been applied in personalized adaptive education systems to address content delivery-related learning challenges. However, not much is known about content adaptation based on bioinspired optimization algorithms. This study offers a comprehensive evaluation of the literature on personalized adaptive learning Management systems based on bioinspired optimization algorithms. The study examined conference proceedings and journal papers published in Scopus, Web of Science, and IEEE databases between 2013 and 2023. Nevertheless, Web of Science yielded no papers that were connected to this investigation. Web of Science was thus left out of the research. 5442 were screened in total, 303 were evaluated, and 6 were deemed eligible for the systematic review. Our findings suggest that there have been a limited number of research or personalized adaptive learning systems based on bioinspired algorithms.

Introduction

The original version of this paper is available on https://www.scientific.net/EI.14.139.pdf

The rapid advancement in artificial intelligence (AI) technologies has led to significant interest in their ability to address challenges in every field and education is no exception [1]. The emergence of sophisticated AI-enabled learning systems is gaining popularity, due to their ability to deliver learning content and adapt to their specific requirements [2]. AI is a technology that seeks to mimic human behavior by interpreting human activities and producing desired results. Therefore, the ultimate goal of AI is to perform tasks that would otherwise require human intervention [3]. AI will play a bigger part in higher education since it allows students to approach learning challenges in a way that is specific to their preferences and experiences [4]. Thus, one critical goal of AI in Education is the provision of personalized learning that maximizes learning with tailored guidance or support depending on learning status, preferences, or personalized traits [5]. In addition, AI-enabled learning systems can be used to predict learning performance, attrition, and relevance, enabling instructors to detect at-risk students and provide timely intervention [6]. However, despite the fact that these modern learning systems are helpful as educational tools that satisfy student needs, there are not many systems in place that are intended to address the concerns and problems faced by many students [2].

The quality of knowledge is the most important task of a learning system at any level and stage of education [7]. In addition, the learning process has become more convenient due to digital learning platforms such as Learning Management Systems (LMS) as indicated in Fig. 1 [8]. For instance, to improve online learning capabilities, LMS has been embraced by the majority of universities, organisations, and institute worldwide. It is a tactical approach to organizing, producing, delivering, overseeing, and maintaining educational events or courses [9]. However, current LMS do not consider students' individuality regarding their various levels of input knowledge and skills or their different learning styles [10]. Moreover, the quality of LMS also offers a dynamic platform for fostering communication between teachers and students [9]. Hence, the use of adaptive knowledge control has
become relevant in considering the individual level of knowledge of the learners [7]. Although, a personalized adaptive learning is an efficacious method that leverages on technology to promptly modify instructional strategies based on learners' specific variations and shifts in traits, output, and personal development in real-time [11]. Thus, in light of student's objectives and level of expertise, an adaptive learning system seeks to alter the way the content is delivered to a specific student [12].

Fig. 1. Learning management system model.

Fig. 1. Learning management system model.

Additionally, an effective adaptive learning management system can give fast, accurate, and highly targeted feedback during problem-solving. This means that it can organize the content according to the unique skills of each student [13]. Typically, an adaptive platform facilitates personalized learning and has the potential to boost learning outcomes and improve the student's learning experience and it represents an opportunity to ensure equity for all students globally. Thus, adaptive learning environments are essential in addressing the large-scale personalization for realworld human learning processes [14]. In this regard, there are a number of AI techniques and approaches that can be utilized to evaluate educational data, to explore instructor and student actions in identifying behavioural trends and translate the educational data into insights that can be leveraged on to enhance the learning experience [13]. For instance, Deep Learning (DL) and Machine Learning (ML) are two subsets of AI, that are thought to revolutionize the field of education. Thus AI, including ML, and DP techniques can be applied to education data to enhance digital education [4].
[11] has established from previous literature that AI techniques and approaches have been employed to classify and collect student data based on their needs and attributes. Furthermore, the way in which the data is processed to create an intelligent and adaptive learning environment determines how effective a personalised adaptive education system will be. In this regard, the techniques and approaches rely on algorithms to automatically and dynamically map the behavioural traits of students to a particular approach of learning with the goal to optimise each student's unique learning experience [11]. Other examples of learning-related AI techniques are Artificial Neural Networks (ANN) known as one of the deep learning techniques, Naïve Bayes and Random forest classification in data mining, Support Vector Machines (SVM), decision trees for classification, regression and outlier detection. Furthermore, knowledge-based reasoning and inquiries can be guided by metaheuristics in complex scenarios and challenges, allowing the mediation of more precise, effective, and dependable answers. Further, conclusions from past and current information could be drawn using inductive and deductive reasoning [3]. Another developing crucial element of LMS is Learning Analytics (LA), and deep learning-based approach that tracks and logs student activities in higher education's online environment to improve instructions. Therefore, based on the students' log activity that an LMS provides, LA assesses the gathered data and forecasts student performance. The main goal is to identify students who are at risk so that timely and suitable interventions can be given [15].

Bioinspired Algorithms

The original version of this paper is available on https://www.scientific.net/EI.14.139.pdf

Nature serves as the source of inspiration for bioinspired algorithms. Similarly, these algorithms are also called metaheuristics and are based on the same fundamental concepts of animals, birds and plants behavior [16]. Therefore, solving complicated real-life problems has become a crucial duty in modern-day society. Many of these constitute combinational problems, where finding optimal solutions is preferred than finding exact solutions. It has been established that traditional optimization techniques work well for small-scale problems. Therefore, conventional approaches either don't scale up well enough to solve large-scale problems in the real world, don't produce the best results, or only provide answers after a lengthy process. Furthermore, even previous AI-based methods used to solve the issues were unable to produce satisfactory outcomes [17]. Moreover, despite the fact that numerical approaches have been frequently employed to solve optimisation problems, their precision depends on the selection of an initial solution. Additionally, analytical methods are almost of no use in real-world applications due to unknown spaces and the complex, non-convex character of the problems. As a result, a unique class of intelligent search algorithms such as metaheuristic optimisation techniques has been developed in response to the shortcomings of analytical mathematical methods in problem-solving [18]. These methods, also termed bioinspired algorithms that drew inspiration from the characteristics and behaviours of organisms have yielded a wide range of usefulness. Through simulations, they have been theoretically proven and implemented [17].

Therefore, Bio-Inspired Optimisation (BSO) techniques facilitate the resolution of difficult optimisation problems while simultaneously addressing the drawbacks of traditional optimisation strategies [19]. In addition, BSO is recognized as a nature-inspired evolutionary algorithm for feature selection among several well-known conventional feature selection strategies. It converges to a global optimum rather than a local one and has a low computing cost [19]. Its local optimal avoidance property aids in the search for global solutions. In metaheuristic approach utilization, the task is split into two phases: exploration and exploitation. Exploitation is the process of focusing on a particular area to identify the best answer in the area, whereas exploration is the search for larger areas with potential solutions [17]. Moreover, the notion of exploration denotes the algorithm's capacity to search the space globally to find the ideal area, while the notion of exploitation denotes the algorithm's capacity to search the space locally to converge a better space. Therefore, achieving an equilibrium between exploration and exploitation is necessary in identifying the best optimal solution [18]. Overall, to optimize an existing LMS learning platform and address concerns about its adaptability, new computing methods built on BSO algorithms or evolutionary concepts must be adopted [20]. It is important to note that the high computational complexity and delayed convergence rates of BSO algorithms are their drawbacks. However, these disadvantages can be overcome through hybridizing with other approaches and techniques to enhance the performance of BSO algorithms and prevent premature convergence in the future [19].

Methodology

The original version of this paper is available on https://www.scientific.net/EI.14.139.pdf

Since the results of our search were limited, the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) steps were altered for this inquiry. PRISMA mandates that a substantial sample of data must be scrutinized and evaluated. Thus, as illustrated in the six phases in Fig. 2 below, we used the Systematic Literature Review (SLR) approach. With the use of this technique, a thorough literature search was carried out in the bibliographic databases IEEE and Scopus, which include academic writing from almost every discipline of study. We also considered and searched Web of Science database. However, we found that there were no publications about bioinspired algorithm-based personalised adaptive learning management systems. Moreover, SLR is an important tool for carrying out thorough systematic reviews, since it helps to pinpoint gaps and investigate novel research exploration. As a result, SLR is a tool that may be used to create, assess, and analyze outcomes for a specific topic [21,22]. Therefore, the focus is on accurately extracting high-quality data from a pool of data to ensure excellent reporting [23]. In this regard, the SLR was used in this study to assess the body of information that is currently available for professional practice and identify any knowledge gaps in the area of study [24].

Fig. 2. Systematic literature review method.

Fig. 2. Systematic literature review method.

The six phases involved in conducting a systematic literature review are as follows:

Research Identification. The objective of this study was to focus on studies related to personalized adaptive LMS based on bioinspired algorithms. Furthermore, the goal of the SLR was to identify a gap in adaptive learning management systems (LMS) using bioinspired algorithms.

Research Strategy. The study relied on Endnote referencing for organizing, screening and bibliographic management. Moreover, Endnote is useful in capturing the reference details during the screening of the articles and conducting searches from multiple sources [21]. Additionally, the following keywords (Adaptive Learning Management System, Adaptive Personalized Learning, Personal Learning Environment, Bioinspired, optimization algorithm, Metaheuristic algorithm and Bioinspired intelligent algorithm). These keywords were used to extract articles from the databases.

Study Selection. The publications in this section were chosen based on inclusion and exclusion criteria that stated only English-language papers published in scholarly journals would be taken into consideration, as well as papers that were pertinent to the context of the study and contained novel findings that warranted additional investigation. Conversely, non-accessible journal publications were not included. As a result, six published journal articles and conference papers made up the final selection. Additionally, reviewing the article's title, abstract, and body helped determine how relevant the article was to the context of the search. As a result, 1066 were screened, 441 articles were assessed and evaluated, and 6 qualified for the systematic review. Furthermore, every publication that was included in the systematic review process was considered and pertinent to the research project [21].

Quality Assessment. In this section, we assessed the robustness and credibility of the article selection process using quality criteria. In terms of systematic review analysis, the criteria taken into consideration for evaluating the articles on the research paper that were extracted from the databases adequately addressed the research-related insights.

Data Extraction. The articles were selected from the IEEE and Scopus databases as indicated in the methodology section above. Furthermore, the articles were considered on the basis of the inclusion and exclusion criteria. Thereafter, data was extracted from the qualified articles as explained in the next step, number 6.

Data Synthesis and Analysis. This section elucidates the methodology used to undertake the modified PRISMA approach to the SLR method in light of the small number of publications included in this research paper. In addition, step 2 indicates the keywords and databases used for this study as mentioned in the methodology section. In step 3, which is the selection phase, we had initially retrieved 1377 articles from both Scopus and IEEE databases. 36 items, however, were eliminated as duplicates. Thereafter, 1066 records were screened. Step 4, represents the quality assessment stage. In this step, data synthesis and analysis, 441 articles underwent assessment and evaluation. Thereafter, 6 publications were then determined to be eligible for this research paper.

Results and Discussion

The original version of this paper is available on https://www.scientific.net/EI.14.139.pdf

This systematic review discusses the six studies that are relevant to our research project. Data was collected using the methods described in the methodology section of the six included articles, four articles employed bioinspired algorithms, one depended on the Learning analysis method, and the other on selection criteria. Four of the articles were conference papers, while two were published in
electronic journals. As Table 1 shows, these articles were the primary source of information and were published in scholarly journals. Furthermore, Table 1 lists the adaptive techniques and datasets employed in the chosen studies. Each study used a different approach to modify the online learning management system. Additionally, to investigate the distribution of the research publications, we employed statistical analysis. As seen in Fig. 3, the distribution of the research publications was as follows: India in 2013, Serbia in 2021, China in 2021, Thailand in 2021, Malaysia in 2021, and Kazakhstan in 2023. On the other hand, Fig. 4 shows how publications are distributed by year. Each country produced one paper. Further, we were able to establish significant facts about personalized adaptive learning management systems based on bioinspired techniques. For example, we collected information on how bioinspired algorithms are capable of offering the most efficient solution. Furthermore, based on this research paper the findings indicate that the usage of personalized adaptive learning environments based on bioinspired algorithms is promising and growing. In addition, the research paper revealed the significance of using bioinspired algorithms for deploying innovative E-learning mechanisms in higher institutions of learning. This concurs with [20] who emphasizes the importance of considering new computing methods built on BSO algorithms or evolutionary concepts to optimize an existing LMS learning platform and address concerns about adaptability.

Table 1. Presents the adaptive methods and dataset.

Author | Adaptative method | Dataset
[25] | Learning analysis methods | Activity report activity report
[26] | Selection criteria | Assessment scores
[27] | algorithms based on a unified artificial immune system (UAIS) | Statistical data
[28] | Particle swarm optimization (PSO) and Jaya algorithms | Discrete data
[29] | metaheuristic algorithm of Particle Swarm Optimization (PSO) | Real data
[30] | Genetic Algorithm (GA) and Ant Colony Optimization Algorithm (ACO) | Course and jobs

Fig. 4 shows the number of published articles for each country. Each country produced one paper.

Fig. 3. Publication per country.

Fig. 3. Publication per country.

Fig. 4 displays the total number of selected published journal articles per year. Further, 1 journal article was published in 2013 and 2023, and 2 journal articles were published in 2021.

Fig. 4. Number of selected published journal articles between 2013 and 2023.

Fig. 4. Number of selected published journal articles between 2013 and 2023.

[25] work was able to show that the learning analysis method may be used to provide adaptive learning. According to the study's findings, learners of the two styles differ in the order of their learning behaviours but not much in their performance and that there is a difference in the critical behaviour sequence that predicts learning performance. On the other hand, [26] relied on the selection criteria which consisted of learning style preferred language of reporting and assessment method. Conversely, [29] demonstrated that Particle Swam Optimisation (PSO) may record, examine, and group data according to individual performance concerning precision, effectiveness, and level of understanding. The technique of carrying out the simulation involved actual data that was gathered from students. Notably, [28] used the Jaya and PSO algorithms to automatically create collaborative learning groups, to improve the efficiency of online learning as a whole. When compared to PSO, the Jaya algorithm proved to be the best option. A multi-objective optimization model was created in a different study by [30] to determine the best learning paths. The results indicated that, in terms of efficiency, the Genetic Algorithm (GA) performed slightly better than Ant Colony Optimisation (ACO) in providing the runtime response for a single request. In a similar study, the complexity of personalizing courses for individual students was addressed by using an algorithm based on the Unified Artificial Immune System (UAIS) [27].

Conclusion

The original version of this paper is available on https://www.scientific.net/EI.14.139.pdf

In this research paper, we presented a comprehensive systematic literature review on personalized adaptive learning management systems based on a bioinspired optimization algorithm. Our findings indicate the scarcity of personalized adaptive learning management systems based on bioinspired optimization algorithms. The results of the studies in this systematic review indicate that further research is required to examine alternative research directions to improve the quality, impact, and pace of convergence of the suggested algorithms. Furthermore, scholars and researchers may utilise a hybrid algorithm that blends both conventional and bioinspired methods to address the challenge posed by the complexity of learning management system adaptation.

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The original version of this paper is available on https://www.scientific.net/EI.14.139.pdf

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Keywords Index

3

3D Printing Smart Architecture: Case Studies and Breakthrough Applications of Advanced Materials in Buildings; Biomimetic Robot with Peristaltic Locomotion Integrating 3D Printing and IoT Technologies

A

Adaptive Learning Personalized Adaptive Learning Management System Based on Bioinspired Algorithm: A Systematic Review

Adsorption Tunable Hydrophobicity and Photoluminescence in Polystyrene-Calcium Carbonate Composites with Citric Acid-Induced Porosity

Advanced Materials Smart Architecture: Case Studies and Breakthrough Applications of Advanced Materials in Buildings

Air Pollution Implementation of Boosting Algorithms in Predicting Air Quality Index of South Indian Cities

Air Quality Index (aqi) Implementation of Boosting Algorithms in Predicting Air Quality Index of South Indian Cities

Artificial-super-intelligence (asi) PRISMA Approach for Assessing Fingerprint Classification Models Based on Artificial Super-Intelligence Techniques

B

Bioinspired Optimization Algorithm Personalized Adaptive Learning Management System Based on Bioinspired Algorithm: A Systematic Review

Biomimetic Robots Biomimetic Robot with Peristaltic Locomotion Integrating 3D Printing and IoT Technologies

Biomimicry Smart Architecture: Case Studies and Breakthrough Applications of Advanced Materials in Buildings

Building Envelopes Smart Architecture: Case Studies and Breakthrough Applications of Advanced Materials in Buildings

C

Calcium Carbonate Tunable Hydrophobicity and Photoluminescence in Polystyrene-Calcium Carbonate Composites with Citric Acid-Induced Porosity

Citric Acid Tunable Hydrophobicity and Photoluminescence in Polystyrene-Calcium Carbonate Composites with Citric Acid-Induced Porosity

Cloud Computing An Improved Data Integrity Verification Model in Cloud Computing

Concrete Durability Study of Kevlar Aramid Fiber in Improving Concrete Performance and Mitigating Pathologies

Cryptographic Accumulator Provable Data Possession-merkle Hash Tree Algorithm An Improved Data Integrity Verification Model in Cloud Computing

D

Data Integrity An Improved Data Integrity Verification Model in Cloud Computing

Data Verification An Improved Data Integrity Verification Model in Cloud Computing

Discharge Analysis Analysis of the Effect of Discharge Needs on Pesangan Hydropower Plant in Central Aceh Regency

E

Ensemble Stack Implementation of Boosting Algorithms in Predicting Air Quality Index of South Indian Cities

F

Fingerprint-classification PRISMA Approach for Assessing Fingerprint Classification Models Based on Artificial Super-Intelligence Techniques

G

Great Potential Challenges in Water Energy Development for Energy Security in Aceh

H

Hospitals IoT Based Environmental and Water Monitoring System for Sterilization Units in Honduras

Hybrid Energy Study of Solar and Wind Energy Potential for Power Generation in Pulo Aceh

Hydropower Plant Analysis of the Effect of Discharge Needs on Pesangan Hydropower Plant in Central Aceh Regency

I

Internet Of Things Implementation of an IOT Sensor Network and Machine Learning to Measure the Water Quality

Internet Of Things (iot) Biomimetic Robot with Peristaltic Locomotion Integrating 3D Printing and IoT Technologies

Iot IoT Based Environmental and Water Monitoring System for Sterilization Units in Honduras

K

Kevlar Reinforcement Study of Kevlar Aramid Fiber in Improving Concrete Performance and Mitigating Pathologies

Kinetic Elements Smart Architecture: Case Studies and Breakthrough Applications of Advanced Materials in Buildings

L

Learning Management System Personalized Adaptive Learning Management System Based on Bioinspired Algorithm: A Systematic Review

Lightgbm Implementation of Boosting Algorithms in Predicting Air Quality Index of South Indian Cities

M

Machine Learning Implementation of an IOT Sensor Network and Machine Learning to Measure the Water Quality

Mechanical Performance Enhancement Study of Kevlar Aramid Fiber in Improving Concrete Performance and Mitigating Pathologies

Monitoring IoT Based Environmental and Water Monitoring System for Sterilization Units in Honduras

O

Oleic Acid Tunable Hydrophobicity and Photoluminescence in Polystyrene-Calcium Carbonate Composites with Citric Acid-Induced Porosity

P

Peristaltic Locomotion Biomimetic Robot with Peristaltic Locomotion Integrating 3D Printing and IoT Technologies

Photoluminescence Tunable Hydrophobicity and Photoluminescence in Polystyrene-Calcium Carbonate Composites with Citric Acid-Induced Porosity

Polystyrene Tunable Hydrophobicity and Photoluminescence in Polystyrene-Calcium Carbonate Composites with Citric Acid-Induced Porosity

Porous Composites Tunable Hydrophobicity and Photoluminescence in Polystyrene-Calcium Carbonate Composites with Citric Acid-Induced Porosity

Preferred Reporting Items For Systematic Reviews And Meta-analysis (prisma) PRISMA Approach for Assessing Fingerprint Classification Models Based on Artificial Super-Intelligence Techniques

Pulo Aceh Study of Solar and Wind Energy Potential for Power Generation in Pulo Aceh

R

Real-time Data Collection Biomimetic Robot with Peristaltic Locomotion Integrating 3D Printing and IoT Technologies

Renewable Energy Challenges in Water Energy Development for Energy Security in Aceh

Robotics Biomimetic Robot with Peristaltic Locomotion Integrating 3D Printing and IoT Technologies

S

Sensor Network Implementation of an IOT Sensor Network and Machine Learning to Measure the Water Quality

Smart Architecture Smart Architecture: Case Studies and Breakthrough Applications of Advanced Materials in Buildings

Smart Materials Smart Architecture: Case Studies and Breakthrough Applications of Advanced Materials in Buildings

Solar Energy Study of Solar and Wind Energy Potential for Power Generation in Pulo Aceh

Sterilization IoT Based Environmental and Water Monitoring System for Sterilization Units in Honduras

Structural Pathology Mitigation Study of Kevlar Aramid Fiber in Improving Concrete Performance and Mitigating Pathologies

Surface Modification Tunable Hydrophobicity and Photoluminescence in Polystyrene-Calcium Carbonate Composites with Citric Acid-Induced Porosity

Systematic Literature Review Personalized Adaptive Learning Management System Based on Bioinspired Algorithm: A Systematic Review

Systematic Review PRISMA Approach for Assessing Fingerprint Classification Models Based on Artificial Super-Intelligence Techniques

W

Water Energy Challenges in Water Energy Development for Energy Security in Aceh

Water Needs Analysis of the Effect of Discharge Needs on Pesangan Hydropower Plant in Central Aceh Regency

Water Quality IoT Based Environmental and Water Monitoring System for Sterilization Units in Honduras; Implementation of an IOT Sensor Network and Machine Learning to Measure the Water Quality

Wind Energy Study of Solar and Wind Energy Potential for Power Generation in Pulo Aceh

X

Xgboost Implementation of Boosting Algorithms in Predicting Air Quality Index of South Indian Cities

Authors Index

A

Adekanmi, D. Tunable Hydrophobicity and Photoluminescence in Polystyrene-Calcium Carbonate Composites with Citric Acid-Induced Porosity

Adria, A. Study of Solar and Wind Energy Potential for Power Generation in Pulo Aceh

Alvarado-Orellana, B. Biomimetic Robot with Peristaltic Locomotion Integrating 3D Printing and IoT Technologies

Aremu, A. Tunable Hydrophobicity and Photoluminescence in Polystyrene-Calcium Carbonate Composites with Citric Acid-Induced Porosity

D

Desmi, A. Analysis of the Effect of Discharge Needs on Pesangan Hydropower Plant in Central Aceh Regency

E

Ersa, N. Analysis of the Effect of Discharge Needs on Pesangan Hydropower Plant in Central Aceh Regency

F

Fajardo, J. Biomimetic Robot with Peristaltic Locomotion Integrating 3D Printing and IoT Technologies

Frighi, V. Smart Architecture: Case Studies and Breakthrough Applications of Advanced Materials in Buildings

H

Hafli, T. Analysis of the Effect of Discharge Needs on Pesangan Hydropower Plant in Central Aceh Regency

K

Khomo, F. Personalized Adaptive Learning Management System Based on Bioinspired Algorithm: A Systematic Review

Khumalo, N. An Improved Data Integrity Verification Model in Cloud Computing

L

Lagos, F. IoT Based Environmental and Water Monitoring System for Sterilization Units in Honduras

M

Madrid, J. Implementation of an IOT Sensor Network and Machine Learning to Measure the Water Quality

Martinez-Rangel, M. Implementation of an IOT Sensor Network and Machine Learning to Measure the Water Quality

Mathonsi, T. An Improved Data Integrity Verification Model in Cloud Computing

Mendes, A. Study of Kevlar Aramid Fiber in Improving Concrete Performance and Mitigating Pathologies

Mendez, J. Tunable Hydrophobicity and Photoluminescence in Polystyrene-Calcium Carbonate Composites with Citric Acid-Induced Porosity

Millham, R. PRISMA Approach for Assessing Fingerprint Classification Models Based on Artificial Super-Intelligence Techniques; Personalized Adaptive Learning Management System Based on Bioinspired Algorithm: A Systematic Review

Mishra, D. Implementation of Boosting Algorithms in Predicting Air Quality Index of South Indian Cities

Molina, C. IoT Based Environmental and Water Monitoring System for Sterilization Units in Honduras

Mustafa, A. PRISMA Approach for Assessing Fingerprint Classification Models Based on Artificial Super-Intelligence Techniques; Personalized Adaptive Learning Management System Based on Bioinspired Algorithm: A Systematic Review

N

Naman, S. Implementation of Boosting Algorithms in Predicting Air Quality Index of South Indian Cities

O

Ojo, S. An Improved Data Integrity Verification Model in Cloud Computing

Olorunnisola, A. Tunable Hydrophobicity and Photoluminescence in Polystyrene-Calcium Carbonate Composites with Citric Acid-Induced Porosity

Olowofoyeku, A. Tunable Hydrophobicity and Photoluminescence in Polystyrene-Calcium Carbonate Composites with Citric Acid-Induced Porosity

P

Perdomo, F. IoT Based Environmental and Water Monitoring System for Sterilization Units in Honduras

Putri, S. Analysis of the Effect of Discharge Needs on Pesangan Hydropower Plant in Central Aceh Regency

Q

Quintanilla, O. Implementation of an IOT Sensor Network and Machine Learning to Measure the Water Quality

R

Rizki, A. Challenges in Water Energy Development for Energy Security in Aceh

S

Sahu, A. Implementation of Boosting Algorithms in Predicting Air Quality Index of South Indian Cities

Sandoval-Ferrufino, J. Biomimetic Robot with Peristaltic Locomotion Integrating 3D Printing and IoT Technologies

Santos, D. Study of Kevlar Aramid Fiber in Improving Concrete Performance and Mitigating Pathologies

Sara, I. Study of Solar and Wind Energy Potential for Power Generation in Pulo Aceh

Setiawan, I. Challenges in Water Energy Development for Energy Security in Aceh

Syuhada, A. Study of Solar and Wind Energy Potential for Power Generation in Pulo Aceh

T

Tarmizi, T. Study of Solar and Wind Energy Potential for Power Generation in Pulo Aceh

Thaib, R. Challenges in Water Energy Development for Energy Security in Aceh

U

Umar, F. Analysis of the Effect of Discharge Needs on Pesangan Hydropower Plant in Central Aceh Regency

Y

Yang, H. PRISMA Approach for Assessing Fingerprint Classification Models Based on Artificial Super-Intelligence Techniques

Z

Zulfan, Z. Challenges in Water Energy Development for Energy Security in Aceh