Distance Based Triggering and Dynamic Sampling Rate Estimation for Fuzzy Systems in Communication Networks

  • Clement N. Nyirenda Department of Computational Intelligence and Systems Science Tokyo Institute of Technology G3-49, 4259 Nagatsuta, Midori-ku, Yokohama 226-8502, Japan
  • Fangyan Dong Department of Computational Intelligence and Systems Science Tokyo Institute of Technology G3-49, 4259 Nagatsuta, Midori-ku, Yokohama 226-8502, Japan.
  • Kaoru Hirota Department of Computational Intelligence and Systems Science Tokyo Institute of Technology G3-49, 4259 Nagatsuta, Midori-ku, Yokohama 226-8502, Japan.

Abstract

To reduce computational cost in fuzzy systems in communication networks, distance based triggering and sampling rate adaptation probabilities are proposed based on the concept of probability via expectation. The triggering probability, which is calculated by using the square of distance between subsequent input vectors, governs the rate at which the fuzzy system is triggered. The dynamic sampling rate probability, which governs the adaptation of the sampling rate, is computed by using the exponentially weighted moving average (EWMA) of the triggering probability. A stopping criterion, based on convergence tests, is also proposed to ensure that the mechanism switches off when the sampling period has converged. The triggering mechanism reduces the number of computations in the Fuzzy Logic Congestion Detection (FLCD) in wireless Local Area Networks (WLANs) by more than 45%. Performance, in terms of packet loss rate, delay, jitter, and throughput, however, remains virtually the same. On the other hand, the dynamic sampling rate mechanism leads to more than 150% improvement in sampling rate and more than 70% reduction in fuzzy computations while performance in the other key metrics remains virtually the same. As part of future work, the proposed mechanism will be tested in fuzzy systems in wireless sensor/actuator networks.

References

[1] M. Spott, K. Leiviska, and T. Martin: Roadmap Contribution IBA C Applications in Telecommunications, Multimedia and Services, European Network on Intelligent Technologies (EUNITE) for Smart Adaptive Systems (SAS), July 2004.

[2] Y.L. Chen, J.W. Wang, Y.S. Lin, and J.H. Wen: Combined Fuzzy-Based Power Control with Window-Based Transmission Rate Management in Multimedia CDMA Cellular Systems, International Journal of Electronics and Communications, doi:10.1016/j.aeue.2010.04.009, 2 June 2010.
http://dx.doi.org/10.1016/j.aeue.2010.04.009

[3] C. Chrysostomou, A. Pitsillides, A. Sekercioglu: Fuzzy Explicit Marking: A Unified Congestion Controller for Best-effort and Diff-serv Networks, Computer Networks Journal (COMNET), Vol. 53, No. 5, pp.650-667, 9 April 2009.
http://dx.doi.org/10.1016/j.comnet.2008.11.002

[4] C.N. Nyirenda and D.S. Dawoud: Multi-objective Particle Swarm Optimization for Fuzzy Logic Based Active Queue Management, in Proc. of the IEEE International Conference in Fuzzy Systems, Vancouver, Canada, pp. 2231-2238, July 2006.
http://dx.doi.org/10.1109/fuzzy.2006.1682010

[5] M. Balakrishnan and E. E. Johnson: Fuzzy diffusion analysis: Decision significance and applicable scenarios, Proc. of IEEE Military Communications Conference, no. 1, pp. 2175- 2181, October 2006.
http://dx.doi.org/10.1109/milcom.2006.302180

[6] M. Marin-Perianu and P. J. M.Havinga: D-FLER: A distributed fuzzy logic enginefor rulebased wireless sensor networks, Proc. of International Symposium on Ubiquitous Computing Systems (UCS), pp. 86–101, 2007.
http://dx.doi.org/10.1007/978-3-540-76772-5_7

[7] F. Xia, W.H. Zhao, Y.X. Sun, Y.C. Tian: Fuzzy Logic Control Based QoS Management in Wireless Sensor/Actuator Networks, Sensors, Vol. 7, No.12, pp. 3179-3191, 2007.
http://dx.doi.org/10.3390/s7123179

[8] C.N. Nyirenda, F. Dong, and K. Hirota: Euclidean Distance Based Triggering of Fuzzy Systems in Communication Networks, In proceedings of the International Symposium on Intelligent Systems (iFAN 2010),Tokyo, Japan, September 2010.

[9] P. Whittle, Probability via Expectation, 4th ed., Springer-Verlag, New York, 2000.
http://dx.doi.org/10.1007/978-1-4612-0509-8

[10] D.A. McQuarrie: Mathematics for physical chemistry, pp. 124, Univ. Science Books, 2008.

[11] J.K. Hunter and B. Nachtergaele: Applied analysis, World Scientific, 2001.
http://dx.doi.org/10.1142/4319

[12] B. Richmond and T. Richmond, A Discrete Transition to Advanced Mathematics, AMS Bookstore, 2009.

[13] C.N. Nyirenda, D.S. Dawoud, F. Dong, M. Negnevitsky, and K. Hirota: A Fuzzy Multiobjective Particle Swarm Optimized TS Fuzzy Logic Congestion Controller for Wireless Local Area Networks, Journal of Advanced Computational Intelligence and Intelligent Informatics (JACIII), Vol.15, No.1, pp. 41-54, January 2011.

[14] NS2 network simulator, http://www.isi.edu/nsnam/ns/, Accessed on 28 June, 2010.

[15] E.H. Mamdani and S. Assilian, An experiment in linguistic synthesis with a fuzzy logic controller, International Journal of Man-Machine Studies, Vol. 7, No. 1, pp. 1-13, 1975.
http://dx.doi.org/10.1016/S0020-7373(75)80002-2

[16] T. Takagi and M. Sugeno, Fuzzy identification of systems and its applications to modeling and control, IEEE Trans. on Systems,Man and Cybernetics, Vol.15, 116-132, 1985.
http://dx.doi.org/10.1109/TSMC.1985.6313399

[17] R. Pan, B. Prabhakar, and K. Psounis: Choke - a stateless active queue management scheme for approximating fair bandwidth, Proc. of INFOCOM, pp. 942-951, March 2000.
Published
2011-09-10
How to Cite
NYIRENDA, Clement N.; DONG, Fangyan; HIROTA, Kaoru. Distance Based Triggering and Dynamic Sampling Rate Estimation for Fuzzy Systems in Communication Networks. INTERNATIONAL JOURNAL OF COMPUTERS COMMUNICATIONS & CONTROL, [S.l.], v. 6, n. 3, p. 462-472, sep. 2011. ISSN 1841-9844. Available at: <http://univagora.ro/jour/index.php/ijccc/article/view/2131>. Date accessed: 23 nov. 2020. doi: https://doi.org/10.15837/ijccc.2011.3.2131.

Keywords

communication networks, fuzzy systems, sampling rate