Energy-efficient clustering in wireless sensor networks using metaheuristic algorithms

Energy management in Wireless Sensor Networks (WSNs) remains a critical challenge, particularly in clustering processes. This article compares three optimization algorithms—Grasshopper Optimization Algorithm (GOA), Bat Algorithm (BA), and Whale Optimization Algorithm (WOA)—to achieve energy-efficient clustering and extend network lifetime. Initial cluster head placement is performed using K-means clustering, and a novel cost function is introduced that considers energy consumption and node distribution, enhancing the network’s efficiency and resilience. The algorithms are evaluated across three scenarios with varying base station (BS) placements. In the simplest scenario, with the BS centrally located, GOA slightly outperforms WOA in extending network lifetime, although WOA remains competitive. BA, while energy-efficient, lags behind GOA and WOA. As complexity increases with BS placement at the edge, WOA demonstrates superior energy management, delaying node death and extending network lifetime more effectively than GOA and BA. In the most challenging scenario, where the BS is placed in a remote corner, WOA emerges as the most effective algorithm, maintaining network performance and balancing energy consumption for the longest duration. GOA, while relatively strong, shows faster network lifetime decline, particularly in later stages, whereas BA faces significant challenges, leading to quicker node failures. Overall, this study highlights the importance of efficient clustering and optimization for prolonging WSN lifetimes. WOA excels in complex scenarios, while GOA leads in simpler environments. Integrating K-means clustering with the novel cost function enhances algorithm performance, contributing to the development of resource-efficient WSNs, especially in resource-constrained settings.