Modified Grey Wolf Optimization with Levy Flights Based Energy Efficient Clustering Technique in Wireless Sensor Networks

Abstract

Wireless Sensor Network (WSN) comprises a set of inexpensive, compact and battery powered sensor nodes, deployed in the sensing region. WSN is highly useful for data gathering and tracking applications. Owing to the battery powered nature of sensor nodes, energy efficiency remains as a crucial design issue. Earlier works reported that clustering is considered as an energy efficient technique and effective selection of cluster heads (CHs) remains a major issue in WSN. Since clustering process is considered as an NP hard problem, optimization algorithms are employed to resolve it. This paper develops a new energy efficient clustering technique using Modified Grey Wolf Optimization with Levy Flights (MGWO-LF) for WSN. The proposed MGWO-LF algorithm incorporates the levy flight (LF) mechanism into the hunting phase of traditional GWO algorithm to avoid local optima problem. The proposed model has the ability of proficiently selecting the cluster heads (CHs), achieves energy efficiency and maximum network lifetime. The detailed simulation analysis ensured that the MGWO-LF algorithm has prolonged the network lifetime in a considerable way.