Swarm Intelligence-Based Clustering Algorithms for Wireless Sensor Networks

This work studies the use of swarm intelligence-based clustering algorithms such as Ant Colony Optimization (ACO), Particle Swarm Optimization (PSO) Bee Algorithm (BA), and Firefly Algorithm (FA), to optimize Wireless Sensor Networks. (WSNs). It evaluated their performance by strict simulations on energy consumption level, network lifetime, and clustering quality. It is observed that ACO and PSO consistently showed better results with moderate energy consumption by achieving long network lifetimes as well as precise clustering. However, BA and FA have been competitive but had lower efficiency when looking at certain metrics. The comparative analysis highlights the need for algorithmic selection according to specific demands in WSN deployments. The study complements related research on the application of swarm intelligence in WSN by focusing on routing challenges, energy-aware protocols, and centralized clustering solutions. The results give useful information to practitioners who are looking for the best algorithms, especially in real WSN situations.