A Fuzzy Multiobjective Particle Swarm Optimized TS Fuzzy Logic Congestion Controller for Wireless Local Area Networks
Clement N. Nyirenda*, Dawoud S. Dawoud**, Fangyan Dong*,
Michael Negnevitsky***, and Kaoru Hirota*
*Department of Computational Intelligence and Systems Science, Tokyo Institute of Technology, G3-49, 4259 Nagatsuta, Midori-ku, Yokohama 226-8502, Japan
**School of Electrical, Electronics and Computer Engineering, University of KwaZulu-Natal, King George V Avenue, Durban 4041, South Africa
***School of Engineering, University of Tasmania, Private Bag 65, Hobart, Tasmania 7001, Australia
A Takagi-Sugeno Fuzzy Logic Congestion Detection (TSFLCD) mechanism is proposed for IEEE 802.11 wireless Local Area Networks. A Fuzzy Preference based Multi-Objective Particle Swarm Optimization (FPMOPSO) mechanism, for tuning the input membership functions and the output scalars, is also proposed. An online adaptation mechanism that finetunes the output scalars based on system dynamics is implemented. Compared to the Adaptive Random Early Detection (ARED) and the Mamdani inference based Fuzzy Logic Congestion Detection (FLCD) mechanisms, simulation results show that the TSFLCD mechanism leads to more than 40% reduction in packet loss rate. It also leads to more than 25% and up to 14% reductions in jitter and delay respectively for real time traffic. This work lays a foundation for the development of simple multiobjective fuzzy congestion controllers in wireless LANs.
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Michael Negnevitsky, and Kaoru Hirota, “A Fuzzy Multiobjective Particle Swarm Optimized TS Fuzzy Logic Congestion Controller for Wireless Local Area Networks,” J. Adv. Comput. Intell. Intell. Inform., Vol.15, No.1, pp. 41-54, 2011
Michael Negnevitsky, and Kaoru Hirota, J. Adv. Comput. Intell. Intell. Inform., Vol.15, No.1, pp. 41-54, 2011