Enhancing Wireless Sensor Networks through Modified Jackal Optimization Algorithm for Cluster Head Selection

Abstract

Clustering plays a vital role in prolonging the lifespan of wireless sensor networks (WSNs) by consolidating sensor nodes (SNs) into clusters and assigning cluster heads (CHs) to oversee each cluster's operations. These CHs collect data from their respective cluster nodes and transmit the aggregated information to the base station (BS). However, the selection of an appropriate CH is crucial for enhancing the network's longevity. To address this challenge, an ModifiedJackal Optimization Algorithm (MJOA) is introduced for optimizing cluster head selection in WSNs. Traditional optimization algorithms often struggle to navigate the dynamic and complex network environments effectively. Leveraging insights from the hunting behavior of jackals, the MJOA enhances traditional optimization methods by introducing refined search strategies and adaptive movement mechanisms. This approach aims to improve the convergence speed, solution quality, and scalability of cluster head selection in WSNs. Through extensive simulations and comparisons with Genetic Algorithm (GA), Particle Swarm Optimization (PSO), and Golden Eagle Optimization (GEO), the MJOA demonstrates superior performance, achieving enhanced cluster head selection efficiency, network coverage, and reduced energy consumption. These results underscore the potential of the MJOA as a robust and efficient solution for optimizing WSNs, thereby contributing to the advancement of wireless sensor networks across various applications.