Optimal Clustering Scheme to Minimize Energy Consumption in Wireless Sensor Network

Abstract

Wireless Sensor Network (WSN) are formed by battery-operated devices used for environmental monitoring. Energy plays a vital role in WSN. Still, research is going on to improve the lifetime of sensor nodes. Clustering is an effective topology management technique for increasing the lifetime and scalability of a sensor network. In clustering, the network nodes are grouped into smaller clusters. In each cluster, the Cluster Head (CH) is elected to reduce the burden of the member to base station communication overhead. The main goal of clustering is to identify suitable CH among the group of sensor nodes. In network topology, it is problematic to discover the optimal set of CH nodes. Determining the optimal set of CHs has been established to be a Non-deterministic Polynomial (NP)-hard optimization problem. newlineTo meet the clustering challenges of WSN, a new clustering approach called Fine Tune Cluster Head Selection for Enhance Energy Efficiency is proposed in this research. The main objective of the proposed clustering technique is to increase the network s lifetime and to effectively handle the cluster head selection problem. The contributions of this work are as follows: 1) Proposed a grid formation algorithm for the higher area and network coverage newline2) Derived a fitness function for cluster head selection and routing problem .3) Proposed an improved metaheuristic algorithm for solving clustering problems. 4) Proposed approaches analysed in terms of lifetime, Packet delivery ratio, energy efficiency and throughput etc. newlineA new clustering approach called Fine Tune Cluster Head Selection for Enhance Energy Efficiency is proposed. The fitness function is framed based on the energy and distance of the nodes. An optimization algorithm of the Fine-Tuning Meta-Heuristic Algorithm (FTMA) is used to solve the CH selection problem. The proposed CH selection is compared with other algorithms in terms of energy consumption, lifetime and node mortality rate. Results indicate that the proposed clustering achieves higher efficien