D-FLER – A Distributed Fuzzy Logic Engine for Rule-Based Wireless Sensor Networks

  • Mihai Marin-Perianu
  • Paul Havinga
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4836)


We propose D-FLER, a distributed, general-purpose reasoning engine for WSN. D-FLER uses fuzzy logic for fusing individual and neighborhood observations, in order to produce a more accurate and reliable result. Thorough simulation, we evaluate D-FLER in a fire-detection scenario, using both fire and non-fire input data. D-FLER achieves better detection times, while reducing the false alarm rate. In addition, we implement D-FLER on real sensor nodes and analyze the memory overhead, the numerical accuracy and the execution time.


Sensor Node Membership Function Fuzzy Logic Wireless Sensor Network False Alarm Rate 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
  2. 2.
    AWARE project.
  3. 3.
    Fire Information and Rescue Equipment (FIRE).
  4. 4.
    National Semiconductor LM92 temperature sensor.
  5. 5.
  6. 6.
    Cleary, T., Notarianni, K.: Distributed sensor fire detection. In: International Conference on Automatic Fire Detection (2001)Google Scholar
  7. 7.
    Cleary, T., Ono, T.: Enhanced residential fire detection by combining smoke and co sensors. In: International Conference on Automatic Fire Detection (2001)Google Scholar
  8. 8.
    Costa, A., De Gloria, A., Giudici, F., Olivieri, M.: Fuzzy logic microcontroller. IEEE Micro 17(1), 66–74 (1997)CrossRefGoogle Scholar
  9. 9.
    Roby, R.J., Gottuk, D.T., Peatross, M.J., Beyler, C.L.: Advanced fire detection using multi-signature alarm algorithms. Fire Safety Journal 37, 381–394 (2001)Google Scholar
  10. 10.
    Dannenberg, A.: Fuzzy logic motor control with msp430x14x. Technical Report SLAA235, Texas Instruments (2005)Google Scholar
  11. 11.
    Espinosa, J., Vandewalle, J., Wertz, V.: Fuzzy logic, identification and predictive control. Springer, Heidelberg (2004)Google Scholar
  12. 12.
    Marin-Perianu, M., et al.: Decentralized enterprise systems: A multi-platform wireless sensor networks approach. Technical Report TR-CTIT-07-31, CTIT, University of Twente (2007)Google Scholar
  13. 13.
    Bukowski, R.W., et al.: Performance of home smoke alarms. Technical Report 1455, NIST (2004)Google Scholar
  14. 14.
    Terada, T., et al.: Ubiquitous chip: A rule-based i/o control device for ubiquitous computing. Pervasive, 238–253 (2004)Google Scholar
  15. 15.
    Henkind, S.J., Harrison, M.C.: An analysis of four uncertainty calculi. IEEE Transactions on Systems, Man and Cybernetics 18(5), 700–714 (1988)CrossRefMathSciNetGoogle Scholar
  16. 16.
    Hofmeijer, T., Dulman, S., Jansen, P.G., Havinga, P.J.M.: AmbientRT - real time system software support for data centric sensor networks. In: Intelligent Sensors, Sensor Networks and Information Processing (ISSNIP), pp. 61–66 (2004)Google Scholar
  17. 17.
    Roger Jang, J.S.: Anfis: Adaptive-network-based fuzzy inference system. IEEE Transactions on Systems, Man, and Cybernetics 23, 665–684 (1993)CrossRefGoogle Scholar
  18. 18.
    Kacprzyk, J.: Group decision making with a fuzzy linguistic majority. Fuzzy Sets and Systems 18(2), 105–118 (1986)zbMATHCrossRefMathSciNetGoogle Scholar
  19. 19.
    Levis, P., Culler, D.: Maté: a tiny virtual machine for sensor networks. In: International Conference on Architectural Support for Programming Languages and Operating Systems, pp. 85–95 (2002)Google Scholar
  20. 20.
    Marin-Perianu, M., Hofmeijer, T.J., Havinga, P.J.M.: Implementing business rules on sensor nodes. In: 11th IEEE International Conference on Emerging Technologies and Factory Automation (ETFA), pp. 292–299. IEEE Computer Society Press, Los Alamitos (2006)CrossRefGoogle Scholar
  21. 21.
    Mendel, J.M.: Fuzzy logic systems for engineering: a tutorial. Proceedings of the IEEE 83, 345–377 (1995)CrossRefGoogle Scholar
  22. 22.
    Runkler, T.A., Glesner, M.: Decade - fast centroid approximation defuzzification for real time fuzzy control applications. In: SAC 1994. ACM Symposium on Applied Computing, pp. 161–165. ACM Press, New York (1994)CrossRefGoogle Scholar
  23. 23.
    Strohbach, M., Gellersen, H.W., Kortuem, G., Kray, C.: Cooperative artefacts: Assessing real world situations with embedded technology. In: Ubicomp, pp. 250–267 (2004)Google Scholar
  24. 24.
    Zadeh, L.A.: A computational approach to fuzzy quantifiers in natural languages. Computers and Mathematics 9, 149–184 (1983)zbMATHMathSciNetGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2007

Authors and Affiliations

  • Mihai Marin-Perianu
    • 1
  • Paul Havinga
    • 1
  1. 1.University of Twente, Enschede, The Netherlands 

Personalised recommendations