Dynamic Energy-Efficient Clustering Algorithms Using Advanced Metaheuristics for Prolonged Network Lifetime in Wireless Sensor Networks

Wireless Sensor Networks (WSNs) are pivotal in modern applications, yet challenges such as energy efficiency and network lifetime remain critical. This paper proposes a Dynamic Energy-Efficient Clustering Algorithm (DEECA) utilizing hybrid metaheuristics combining Egret Swarm Optimization and Genetic Algorithms. The proposed method dynamically selects cluster heads and optimizes clustering based on node energy, distance, and traffic conditions. Simulations were conducted on diverse WSN scenarios to evaluate energy consumption, network lifetime, packet delivery ratio, and throughput. Results demonstrate that DEECA outperforms traditional algorithms such as LEACH, HEED, and PSO by achieving a 20% increase in network lifetime and a 15% improvement in packet delivery ratio. Visual analyses include energy consumption trends, coverage heatmaps, and multi-metric radar plots. The findings validate DEECA's capability to enhance energy efficiency and scalability in WSNs, offering a robust framework for real-world deployments in resource-constrained environments.