Data Evaluation in Smart Sensor Networks Using Inverse Methods and Artificial Intelligence (AI): Towards Real-Time Capability and Enhanced Flexibility


Article Preview

Data evaluation is crucial for gaining information from sensor networks. Main challenges include processing speed and adaptivity to system change, both prerequisites for SHM-based weight reduction via relaxed safety factors. Our study looks at soft real time solutions providing feedback within defined but flexible, application-controlled intervals. These can rely on minimizing computation/communication latencies e.g. by parallel computation. Strategies towards this aim can be model-based, including inverse FEM, or model-free, including machine learning, which in practice bases training on a defined system state, too, hence also facing challenges at state changes. We thus introduce hybrid data evaluation combining multi-agent based systems (MAS) with inverse FEM, mainly relying on matrix operations that can be partially distributed: The MAS perform sensor data acquisition, aggregation, pre-computation, and finally application (the LM/SHM itself and higher information processing and visualization layers, i.e., WEB interfaces). System capabilities are evaluated against a virtual test case, demonstrating enhanced stability and reliability. Besides, we analyze system performance under conditions of in-service change and discuss system layouts suited to improve coverage of this issue.



Edited by:

Pietro Vincenzi




S. Bosse et al., "Data Evaluation in Smart Sensor Networks Using Inverse Methods and Artificial Intelligence (AI): Towards Real-Time Capability and Enhanced Flexibility", Advances in Science and Technology, Vol. 101, pp. 55-61, 2017

Online since:

October 2016




* - Corresponding Author

[1] S. Bosse, Unified Distributed Computing and Co-ordination in Pervasive/Ubiquitous Networks with Mobile Multi-Agent Systems using a Modular and Portable Agent Code Processing Platform. In The 6th International Conference on Emerging Ubiquitous Systems and Pervasive Networks (EUSPN 2015), Procedia Computer Science, (2015).


[2] S. Bosse, A. Lechleiter, A hybrid approach for Structural Monitoring with self-organizing multi-agent systems and inverse numerical methods in material-embedded sensor networks. Mechatronics, 2015, doi: 10. 1016/j. mechatronics. 2015. 08. 005, in press.


[3] S. Bosse, Design and Simulation of a Low- Resource Processing Platform for Mobile Multi-Agent Systems in Distributed Heterogeneous Networks. In Agents and Artificial Intelligence, LNAI 8946, Springer, Béatrice Duval, J. van den Herik, S. Loiseau, and J. Filipe, Eds. Springer, (2015).


[4] A. Moraru, M. Pesko, M. Porcius, D. Mladenic, and C. Fortuna1, Using Machine Learning on Sensor Data. Journal of Computing and Information Technology, vol. 4, pp.341-347, (2010).


[5] J. Gama and P. Kosina, Learning Decision Rules from Data Streams. In Proceedings of the Twenty-Second International Joint Conference on Artificial Intelligence, (2011).

[6] V. Di Lecce, M. Calabrese, and C. Martines, From Sensors to Applications: A Proposal to Fill the Gap. Sensors & Transducers, vol. 18, no. Special Isse, pp.5-13, (2013).

[7] D. Lehmhus, J. Brugger, P. Muralt, S. Pane, O. Ergenemann, M. -A. Dubois, N. Gupta, M. Busse, When nothing is constant but change: Adaptive and sensorial materials and their impact on product design. Journal of Intelligent Material Systems and Structures, vol. 24, doi: 10. 1177/1045389X13502855, (2013).


[8] K. Smarsly, K. H. Law, D. Hartmann, Implementation of a multiagent-based paradigm for decentralized real-time structural health monitoring. Proceedings of the Structures Congress 2011, Las Vegas, Nevada, USA, April 14th-16th, 2011, pp.1875-1885.


[9] D. Liang, S. Yuan, Structural health monitoring system based on multi-agent coordination and fusion for large structure. Advances in Engineering Software, vol. 86, pp.1-12, (2015).