Comparative Analysis of Bio inspired Optimization Techniques in Wireless Sensor Networks with GA-PSO Approach

Abstract

Objectives: This research paper is fixated to evaluate the performance of wireless sensor network by employing Bio inspired optimization techniques. In this work, it has been proposed to explore the possibilities of optimization procedures in order to improve the performance of wireless sensor networks. Methods/Analysis: We seek to optimize the Quality of service in wireless sensor network via routing. In order to raise the lifetime of the wireless sensor network load balancing of cluster heads is implemented here in this research work with this the energy consumption could be reduced long with less Error Rate and less Routing Overhead, Minimization of End to end delay and improving Throughput. Findings: In this work, the performance analysis has been evaluated for the different optimization techniques like Genetic algorithm, Particle Swarm optimization, and bacterial foraging optimization and Hybrid approach of GA-PSO optimization. First of all, the optimization techniques such as GA, PSO and BFO are adopted separately on WSN setup and after that the hybridization of GA and PSO is employed. In the existing work Load balancing was employed with GA optimization but in this work other techniques are also taken along with hybridization of GA and PSO. A comparison on the performance analysis of all the optimization algorithms is specified and to infer which of the techniques performs better in order to maximizing the network lifetime and minimizing the end to end delay of the wireless sensor network so that packets transferring is carried efficiently with a reduced amount of error rate so that there will be a lesser chance of the node failure and extend the network lifetime for the awareness of routing optimization. Improvement: Further hybridization of other optimization techniques can be implemented for the improvements of wireless sensor networks. Keywords: Wireless Sensor Networks, Network Performance in Terms of Network Lifetime and End to End Delay, BFO, GA, GA-PSO, PSO