Enhance Wi-Fi Fingerprinting Indoor-Positioning by Error Flag Framework

Abstract:

This research aims to purpose the new method, which is called Error Flag Framework (EFF) to enhance accuracy fingerprinting indoor positioning of wireless device by using machine learning algorithms. EFF is compared with well-known machine learning classifiers; i.e. Decision Tree, Naive Bayes, and Artificial Neural Networks, by exploiting the signal strength from limited information. The performance comparison is done in terms of accuracy of classification of positions, precision of distance classified, and effects of classification of positions on results from quantity of learning data. The result of this study can suggest that EFF can increase performance for indoor positioning of every well-known classifier, especially when the quantity of learning data is large enough. Hence, EFF is the alternate way for implementing in positioning software by using the fingerprinting method.

• Periodical: Advanced Materials Research (Volumes 931-932)
• Pages: 942-946
• DOI: https://doi.org/10.4028/www.scientific.net/AMR.931-932.942
• Online since: May 2014
• Authors: Shutchon Premchaisawatt, Nararat Ruangchaijatupon*
• Keywords: Indoor Positioning, Machine Learning, Wireless Device

References

[1] H. Liu, H. Darabi, P. Banerjee. Survey of wireless indoor positioning techniques and systems. Systems. IEEE Transactions on Systems, Man and Cybernetics, Part C (Applications and Reviews). 2007; 37(6): 1067-1080.

DOI: 10.1109/tsmcc.2007.905750

[2] T.N. Lin, P.C. Lin. Performance comparison of indoor positioning techniques based on location fingerprinting in wireless networks, Wireless Networks Communications and Mobile Computing. 2005; 1569–1574.

DOI: 10.1109/wirles.2005.1549647

[3] E. Mok, G. Retscher. Location determination using WiFi fingerprinting versus Wi-Fi trilateration. Journal of Location Based Services. 2007; 1(2): 145–159.

DOI: 10.1080/17489720701781905

[4] R. Mautz. Overview of current indoor positioning systems. Geodezija ir kartografija. 2009; 35(1): 18-22.

[5] J.R. Quinlan. Induction of decision trees. Machine learning, 1986, 1. 1: 81-106.

[6] G.H. John, P. Langley. Estimating continuous distributions in Bayesian classifiers.

[7] M.T. Hagan, H.B. Demuth, M.H. Beale. Neural network design. Boston: Pws Pub., 1996. pp.2-14.

[8] P. Bahl, V.N. Padmanabhan. RADAR: An in-building RF-based user location and tracking system. In: INFOCOM 2000. Nineteenth Annual Joint Conference of the IEEE Computer and Communications Societies. Proceedings. IEEE. IEEE, 2000. pp.775-784.

DOI: 10.1109/infcom.2000.832252

[9] A. LaMarca, Y. Chawathe, S. Consolvo, J Hightower. Place lab: Device positioning using radio beacons in the wild. In: Pervasive Computing. Springer Berlin Heidelberg, 2005. pp.116-133.

DOI: 10.1007/11428572_8

[10] D. Madigan, E. Einahrawy, R.P. Martin. Bayesian indoor positioning systems. In: INFOCOM 2005. 24th Annual Joint Conference of the IEEE Computer and Communications Societies. Proceedings IEEE. IEEE, 2005. pp.1217-1227.

DOI: 10.1109/infcom.2005.1498348

[11] O.M. Badawy, M.A.B. Hasan. Decision tree approach to estimate user location in WLAN based on location fingerprinting. In: Radio Science Conference, 2007. NRSC 2007. National. IEEE, 2007. pp.1-10.

DOI: 10.1109/nrsc.2007.371395

[12] M. Brunato, R. Battit. Statistical learning theory for location fingerprinting in wireless LANs. Computer Networks, 2005, 47. 6: 825-845.

DOI: 10.1016/j.comnet.2004.09.004

[13] I.H. Witten, E Frank, MA Hall. Data Mining: Practical Machine Learning Tools and Techniques: Practical Machine Learning Tools and Techniques. Elsevier, (2011).

DOI: 10.1016/b978-0-12-374856-0.00015-8