Hybrid PSO-GA-Based Base Station Placement for Balancing Energy Consumption and Delay Among Cluster Heads in Clustered WSNs

Abstract

Optimal base station (BS) placement is critical in enhancing the energy efficiency, latency, and longevity of Wireless Sensor Networks (WSNs). This paper introduces a hybrid metaheuristic approach that combines Particle Swarm Optimization (PSO) and Genetic Algorithm (GA) for dynamic BS positioning in clustered WSNs. The proposed method leverages PSO’s fast convergence and GA’s global exploration to balance energy consumption and communication delay. Embedded within a LEACH-based clustering protocol, the BS location is iteratively optimized based on the distribution of Cluster Heads (CHs). An efficient fitness function, incorporating the standard deviation of energy and latency ensures fairness among CHs. Simulation results demonstrate that the hybrid PSO-GA approach offers enhanced network performance regarding energy efficiency, delay minimization, and extended lifetime.