Hybrid Optimal Energy Management for Clustering in Wireless Sensor Network

Abstract

Energy optimization is the most important to improve the lifetime of a wireless sensor network. Nodes in sensor networks require to have an optimal mechanism for utilizing energy A new technique named Hybrid Optimization Algorithm, presented in this paper, is based on Lagrangian relaxation and entropy for reducing the energy consumption. The proposed model helps to maintain multi-hop connectivity with dynamic action. The nodes are allowed to communicate based on energy, bandwidth, neighbor connectivity, hop count, to maintain high QoS energy parameters. The simulations are in NS2 software. The proposed approach evaluates total consumed energy, average remaining energy, packets received rate, drop rate, normalized routing overhead, delay, throughput, goodput, jitter, and network lifetime. The comparison results show that the nodes' lifetime improved with a Hybrid Optimal Based Cluster Formation (HOBCF) algorithm.