Efficient Search Strategies in Selecting the Best Cluster Heads with Gray Wolf Optimization Based Clustering Technique in WSN

Abstract

Distributed, autonomous sensors are what make up a Wireless Sensor Network (WSN). Nevertheless, the sensor nodes in WSNs run on batteries, therefore power consumption is a major concern. Improving WSNs' durability is largely dependent on the clustering method. This method groups sensors together and chooses leaders from among them (CHs). Cluster Heads collect data from other cluster nodes and relay it to the hub (BS). Selecting the best CHs to maximize network longevity remains the toughest challenge. Here, we present a strategy for choosing group leaders that takes the finest parts of hybrid opposition-based training and the grey wolf efficiency technique and mixes them. The best CHs are chosen using a hybrid approach that dynamically shifts between exploitation and exploration search tactics. The four distinct measures of energy use, minimum distance, centrality, and degree are also used. By choosing the most effective CHs, the suggested selection method improves the effectiveness of the network as a whole. Additionally, the suggested approach is tested in MATLAB and confirmed by a variety of performance measures factors such battery life, network activity, BS location, and packet service efficiency. The suggested approach yields superior results in addition of the quantity of living nodes each round, the maximum amount of packets sent to the BS, the improvement of residual energy, and the enhancement of the lifespan of the network.