A Grey Wolf Optimization-Based Clustering Approach for Energy Efficiency in Wireless Sensor Networks

Abstract

In the realm of Wireless Sensor Networks, the longevity of a sensor node's battery is pivotal, especially since these nodes are often deployed in locations where battery replacement is not feasible. Heterogeneous networks introduce additional challenges due to varying buffer capacities among nodes, necessitating timely data transmission to prevent loss from buffer overflows. Despite numerous attempts to address these issues, previous solutions have been deficient in significant respects. Our innovative strategy employs Grey Wolf Optimization for Cluster Head selection within heterogeneous networks, aiming to concurrently optimise energy efficiency and buffer capacity. We conducted comprehensive simulations using Network Simulator 2, with results analysed in MATLAB, focusing on metrics such as energy depletion rates, remaining energy, node-to-node distance, node count, packet delivery, and average energy in the cluster head selection process. Our approach was benchmarked against leading protocols like LEACH and PEGASIS, considering five key performance indicators: energy usage, network lifespan, the survival rate of nodes over time, data throughput, and remaining network energy. The simulations demonstrate that our Grey Wolf Optimisation method outperforms conventional protocols, showing a 9% reduction in energy usage, a 12% increase in node longevity, a 9.8% improvement in data packet delivery, and a 12.2% boost in data throughput.