Comparison of Particle Swarm and Grey Wolf Bio-Inspired Optimization Algorithms with Leach for Energy Efficient WSN

Wireless Sensor Networks(WSNs) are growing significantly in the research area, employing distributed autonomous sensing machines in many areas like, agriculture monitoring, battlefield surveillance, and home security. Despite of their advantages, WSNs face technical challenges, especially in remote areas with less power sources, necessitating energy-efficient data sensing strategies. Optimizing the selection process of cluster heads in routing protocols is essential, as replacing sensor node batteries in remote locations is impractical thus, extending node lifespan is a primary research focus. Nature-inspired algorithms are increasingly utilized to enhance sensor longevity and transmission distance. Among hierarchical routing algorithms, the Low Energy Adaptive Clustering Hierarchy (LEACH) is widely recognized. This paper is a comparative study of the LEACH and two bio-inspired optimization algorithms- Particle Swarm Optimization (PSO) and Grey Wolf Optimization (GWO). The study evaluates of these algorithms for energy efficiency by examining the number of operational nodes and comparing throughput performance.