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

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

Prioritized Shufflenet Routing in TOAD based 2X2 OTDM Router.

packet-switched network topology, which has been widely used in the optical communication. The adaptive routing algorithm used in shufflenet provides multiple shortest paths from source to destination. Here shufflenet is applied for optical time division multiplexing (OTDM) based networks, where nodes are composed of a terahertz optical asymmetric demultiplexer (TOAD) based 2x2 routers. Here we have introduced a simple priority scheme, where packets are routed via the transmission port (having lower crosstalk compared with the reflection port) of the router as much as possible. Simulation results show that transmission-to-reflection ratio (TRR) greater than one is achievable over a wide range of system load