Evolving Fuzzy Modeling for MANETs Using Lightweight Online Unsupervised Learning

Article

Abstract

The study presents a methodology for evolving fuzzy modeling tasks in Mobile Ad hoc Networks (MANETs) based on distributed data-driven fuzzy clustering and reasoning. The fuzzy clustering is exploited for the purpose of learning fuzzy inference rules online. That calls for one-pass Lightweight Evolving Fuzzy Clustering Method (LEFCM) suitable for deploying on mobile devices with constrained resources in MANETs. There is no standard method to determine the optimal number of fuzzy rules and most of the fuzzy systems still apply the trial and error method, unsuitable for online modeling tasks. The proposed methodology addresses the issues of uncertainties, simplicity and speed to run in non-intrusive way. It estimates online the number of clusters and their centers in the input data space, accordingly the fuzzy rules, by online adaptation of the LEFCM threshold value that affects the number of clusters. Adaptation is based on the combination of geometrical and statistical analyses, as well as on incorporating a multidimensional fuzzy membership degree into the clustering process. The proposed LEFCM is proven by using traditional cluster validity indexes and tested on real data sets.

Keywords

Evolving systems Data-driven fuzzy models Online evolving clustering Mobile Ad hoc networks Unsupervised learning Knowledge and data integration 

Notes

Acknowledgments

The research has been supported by DFG grants N 436BUL112/08. Also thanks goes to Prof. Peter Martini, Nils Aschenbruck, Elmar Gerhards-Padilla from Inst. of Computer Science IV, Bonn University for their strong support and provided data from a realistic disaster area scenario.

References

  1. 1.
    L. Zadeh, Fuzzy sets, Information and Control, Vol. 8, No. 3, pp. 338–353, 1965.MATHCrossRefMathSciNetGoogle Scholar
  2. 2.
    N. Aschenbruck, E. Gerhards-Padilla, M. Gerharz, M. Frank and P. Martini, Modelling mobility in disaster area scenarios. In 10th ACM International Workshop on Modeling Analysis and Simulation of Wireless and Mobile Systems, pp. 4–12, 2007.Google Scholar
  3. 3.
    P. Angelov and N Kasabov, Evolving computational intelligence systems. In I International Workshop on Genetic Fuzzy Systems, 2005.Google Scholar
  4. 4.
    N. Kasabov and Q. Song, DENFIS: dynamic, evolving neural-fuzzy inference systems and its application for time-series prediction, IEEE Transactions on Fuzzy Systems, Vol. 10, No. 2, pp. 144–154, 2002.CrossRefGoogle Scholar
  5. 5.
    P. Angelov, Evolving Takagi-Sugeno fuzzy systems from streaming data, eTS+. In Evolving Intelligent Systems: Methodology and Applications. Wiley, New York, 2010.Google Scholar
  6. 6.
    V. Ravi, E. Srinivas and N. Kasabov, On-line evolving fuzzy clustering. In International Conference on Computational Intelligence and Multimedia Applications, pp. 347–351, 2007.Google Scholar
  7. 7.
    J. de Oliveira and W. Pedrycz, Advances in Fuzzy Clustering and Applications. Wiley, Chichester, 2007.Google Scholar
  8. 8.
    P. Angelov, D. Filev and N. Kasabov, editors., Evolving Intelligent Systems: Methodology and Applications, WileyNew York, 2010.Google Scholar
  9. 9.
    E. Natsheh, A survey on fuzzy reasoning applications for routing protocols in wireless Ad Hoc networks, International Journal of Business Data Communications and Networking, Vol. 4, No. 2, pp. 22–37, 2008.Google Scholar
  10. 10.
    C. Huang, A Bluetooth routing protocol using evolving fuzzy neural networks, International Journal of Wireless Information Networks, Vol. 11, No. 3, pp. 1572–8129, 2004.CrossRefGoogle Scholar
  11. 11.
    X. Xie and G. Beni, A validity measure for fuzzy clustering, IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 13, No. 8, pp. 841–847, 1991.CrossRefGoogle Scholar
  12. 12.
    S. Kwon, Cluster validity index for fuzzy clustering, Electronics Letters, Vol. 34, No. 22, pp. 2176–2177, 1998.CrossRefGoogle Scholar
  13. 13.
    M. Pakhira, S. Bandyopadhyay and U. Maulik, Validity index for crisp and fuzzy clusters, Pattern Recognition, Vol. 37, No. 3, pp. 487–501, 2004.MATHCrossRefGoogle Scholar
  14. 14.
    Iris Data Set in UCI Machine Learning Repository. http://archive.ics.uci.edu/ml/datasets/Iris.
  15. 15.
    The VINT project—network simulator (NS-2). http://www.isi.edu/nsnam/ns.
  16. 16.
    T. Lillesand and R. Kiefer, Minimum Distance to Means Classifier, Digital Image Processing, WileyNew York, 1994. pp. 590–591.Google Scholar
  17. 17.
    R. Ramezani, P. Angelov and X. Zhou, A fast approach to novelty detection in video streams using recursive density estimation. In 4th Inernational IEEE Conference of Intelligent Systems, pp. 14-2–14-7, 2008.Google Scholar
  18. 18.
    J. Martyna, Fuzzy reinforcement learning for routing in wireless sensor networks. In B. Reusch, editor. Computational Intelligence. Theory and Applications, Springer-VerlagBerlin, Heidelberg, New York, 2006. pp. 637–645.CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC 2010

Authors and Affiliations

  1. 1.Institute of Control and System ResearchBulgarian Academy of SciencesSofiaBulgarian

Personalised recommendations