Study on hybrid evolutionary algorithms for energy minimization in a wireless mesh topology towards green computing

Abstract

Next generation wireless network consists of numerous wireless networks for providing better services Wireless Mesh Networks WMNs is the one of the recently emerged wireless technology Collection of mesh routers and mesh clients are called as Wireless mesh network Wireless mesh networks WMNs comprise of mesh routers and mesh clients where minimum mobility for mesh routers is building the WMN Mesh and conventional clients could access the network resources through these mesh routers and also IEEE 80211 IEEE 80215 IEEE 80216 networks are integrated with Wireless mesh networks through gateway and bridging functions of Wireless mesh network Energy consumption plays an important role in achieving Quality of Service in Wireless mesh networks With the Rapid increase of nodes and algorithms in wireless mesh network there is a demand for Artificial neural network based solutions needed for achieving Quality of Service Optimization of energy consumption This thesis derives the issues connected to Optimization of Energy in Wireless Mesh NetworkWMN using Artificial Neural Network Two different algorithms are proposed for effective optimization of energy in wireless networks The first proposed work concentrates on green computing with evolutionary computing model based hybrid algorithm to reduce each node energy consumption in mesh topology NNACOR Neural network ant colony optimized routing is the proposed methodology consists of neural network model and Ant colony algorithm The output of ant colony optimization technique is given to single hidden and output layer of neural network model in hybrid algorithm to find the optimal routing path newline