Cluster Based Improved Particle Swarm Optimization for Optimum Cluster Head Election for Energy Efficient Routing in Wireless Sensor Networks

Abstract

The proposed methodology addresses critical challenges in Wireless Sensor Networks (WSN), focusing on optimizing cluster head and forwarding node selection. Leveraging an enhanced Particle Swarm Algorithm (PSO), the approach prioritizes residual energy and spatial balance in node selection. It efficiently assigns cluster head nodes to ordinary nodes and selects forwarding nodes within clusters. The algorithm incorporates proximity principles to ensure balanced positioning of nodes. Through iterative iterations, the method refines node selections, favoring candidates with higher residual energy and improved spatial distribution. This approach optimizes WSN performance, enhancing data transmission efficiency and network longevity by minimizing energy consumption. Moreover, it reduces communication overhead through piggybacking and ensures dynamic node adaptation for evolving network conditions.