Metaheuristic Algorithms for Energy-Efficient Clustering in Large-Scale Wireless Sensor Networks

Abstract

The proliferation of Large-Scale Wireless Sensor Networks (LS-WSNs) has introduced significant challenges in energy resource management, directly impacting network longevity and operational efficacy. Clustering, a fundamental topology control strategy, has been widely adopted to mitigate energy consumption by aggregating data and reducing transmission distances. However, identifying optimal cluster configurations in LS-WSNs constitutes an NP-hard problem, rendering traditional optimization techniques inadequate. This paper explores the application of metaheuristic algorithms, specifically Genetic Algorithms (GAs) and Particle Swarm Optimization (PSO), for designing energy-efficient clustering protocols. These algorithms offer robust mechanisms for navigating the complex, multi-modal search spaces associated with cluster head selection and cluster formation. By synthesizing current research, this analysis elucidates the core mechanisms through which GAs and PSO enhance network lifetime, reduce energy dissipation, and maintain balanced load distribution across the network. The paper further provides a comparative assessment of their performance, highlights hybrid approaches, and discusses open challenges and future research directions for deploying these metaheuristics in the demanding environments of LS-WSNs.