Abstract
Nowadays, growing interest exists on the integration of artificial intelligence technologies, such as neural networks and fuzzy logic, into Wireless Sensor Networks. However, few attentions have been paid to integrate knowledge based systems into such networks. The objective of this work is to optimize the design of a distributed Fuzzy Rule-Based System embedded in Wireless Sensor Networks. The proposed system is composed of: a central computer, which includes a module to carry out knowledge bases edition, redundant rules reduction and transformation of knowledge bases with linguistic labels in others without labels; access point; sensor network; communication protocol; and Fuzzy Rule-Based Systems adapted to be executed in a sensor. Results have shown that, starting from knowledge bases generated by a human expert, it is possible to obtain an optimized one with a design of rules adapted to the problem, and a reduction in number of rules without a substantial decrease in accuracy. Results have shown that the use of optimized knowledge bases increases the sensor performance, decreasing their run time and battery consumption. To illustrate these results, the proposed methodology has been applied to model the behavior of agriculture plagues.
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References
Karl, H., Willig, A.: Protocols and Architectures for Wireless Sensor Networks. John Wiley & Sons, Chichester (2005)
Akyildiz, I.F., Su, W., Sankarasubramaniam, Y., Cyirci, E.: Wireless sensor networks: A survey. Computer Networks 38(4), 393–422 (2002)
Karlsson, B.: Intelligent Sensor Networks - an Agent-Oriented Approach. In: Workshop on Real-World Wireless Sensor Networks (2005)
Kulakov, A., Davcev, D.: Tracking of unusual events in wireless sensor networks based on artificial neural-networks algorithms. In: Proceedings of the International Conference on Information Technology: Coding and Computing. IEEE, Los Alamitos (2005)
Averkin, A.: Soft Computing in WSNs. In: Proceedings of the EUSFLAT, pp. 387–390 (2007)
Cañada-Bago, J.: From a genetic fuzzy rule-based system to an intelligent sensor network. In: Proceedings of International Conference on Sensor Technologies and Applications, pp. 373–377. IEEE, Valencia (2007)
Marin-Perianu, M., Havinga, P.: D-FLER: A distributed fuzzy logic engine for rule-based wireless sensor networks. In: International Symposium on Ubiquitous Computing Systems (UCS), pp. 86–101 (2007)
Cañada-Bago, J., Gadeo-Martos, M.A., Fernández-Prieto, J.A., Velasco, J.R.: Poster Abstract: A Knowledge Based Wireless Sensor Network. In: Proceeding of European Wireless Sensors Network (EWSN 2009) – Demos/Posters Session, Cork, Ireland, pp. 21–22 (2009)
Sun Microsystems. Home of Project Sun SPOT, http://www.sunspotworld.com/
Mamdani, E.H.: Applications of fuzzy algorithm for control a simple dynamic plant. Proceedings of the IEE 121(12), 1585–1588 (1974)
Cordón, O., Herrera, F., Hoffmann, F., Magdalena, L.: Genetic Fuzzy Systems: Evolutionary tuning and learning of fuzzy knowledge bases. Advances in fuzzy systems – Applications and theory, vol. 19. World scientific Publishing, Singapore (2001)
Bardossy, A., Duckstein, L.: Fuzzy Rule-Based Modeling with Application to Geographical, Biological and Engineering Systems. CRC Press, Boca Raton (1995)
Cordón, O., Herrera, F.: A general study on genetic fuzzy systems. In: Periaux, J., Winter, G., Galán, M., Cuesta, P. (eds.) Genetic Algorithms in Engineering and Computer Science, pp. 33–57. John Wiley and Sons, Chichester (1995)
Carse, B., Fogarty, T.C., Munro, A.: Evolving fuzzy rule based controllers using genetic algorithms. Fuzzy Sets and Systems 80, 273–294 (1996)
Koczy, L.: Fuzzy if ... then rule models and their transformation into one other. IEEE Transactions on Systems, Man, and Cybernetics 26(5), 621–637 (1996)
Driankov, D., Hellendoorrn, H., Reinfrank, M.: An introduction to Fuzzy Control. Springer, Heidelberg (1993)
Lee, C.C.: Fuzzy logic in control systems: fuzzy logic controller- Parts I and II. IEEE Transactions on Systems, Man, and Cybernetics 20(2), 404–418, 419–435 (1990)
Pedrycz, W.: Fuzzy Modelling: Paradigms and Practice. Kluwer Academic Publishers, Dordrecht (1996)
Sugeno, M., Yasura, T.: A fuzzy-logic-based approach to qualitative modeling. IEEE Transactions on Fuzzy Systems 1(1), 7–31 (1993)
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Gadeo-Martos, MÁ., Fernández-Prieto, JÁ., Canada Bago, J., Velasco, JR. (2010). Optimization of Embedded Fuzzy Rule-Based Systems in Wireless Sensor Network Nodes. In: García-Pedrajas, N., Herrera, F., Fyfe, C., Benítez, J.M., Ali, M. (eds) Trends in Applied Intelligent Systems. IEA/AIE 2010. Lecture Notes in Computer Science(), vol 6097. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-13025-0_22
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DOI: https://doi.org/10.1007/978-3-642-13025-0_22
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