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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 fine tunes 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 resulsts 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.

Item Type:	Refereed Article
Keywords:	congestion, fuzzy logic, wireless LAN, particle swarm optimization
Research Division:	Information and Computing Sciences
Research Group:	Machine learning
Research Field:	Neural networks
Objective Division:	Information and Communication Services
Objective Group:	Communication technologies, systems and services
Objective Field:	Communication technologies, systems and services not elsewhere classified
UTAS Author:	Negnevitsky, M (Professor Michael Negnevitsky)
ID Code:	75264
Year Published:	2011
Deposited By:	Centre for Renewable Power Energy Systems
Deposited On:	2012-01-19
Last Modified:	2018-03-18
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