Assessment and improvement of quality of service in wireless networks using fuzzy and hybrid genetic-fuzzy approaches

Mohammad Saraireh, Reza Saatchi, Samir Al-Khayatt, Rebecca Strachan

Research output: Contribution to journalArticlepeer-review

10 Citations (Scopus)

Abstract

Fuzzy and hybrid genetic-fuzzy approaches were used to assess and improve quality of service (QoS) in simulated wireless networks. Three real-time audio and video applications were transmitted over the networks. The QoS provided by the networks for each application was quantitatively assessed using a fuzzy inference system (FIS). Two methods to improve the networks' QoS were developed. One method was based on a FIS mechanism and the other used a hybrid genetic-fuzzy system. Both methods determined an optimised value for the minimum contention window (CW min) in IEEE 802.11 medium access control (MAC) protocol. CW min affects the time period a wireless station waits before it transmits a packet and thus its value influences QoS. The average QoS for the audio and video applications improved by 42.8% and 14.5% respectively by using the FIS method. The hybrid genetic-fuzzy system improved the average QoS for the audio and video applications by 35.7% and 16.5% respectively. The study indicated that the devised methods were effective in assessing and significantly improving QoS in wireless networks.
Original languageEnglish
Pages (from-to)95-111
JournalArtificial Intelligence Review
Volume27
Issue number2-3
DOIs
Publication statusPublished - Mar 2007

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