Hybrid Reinforced Pelican Optimization Algorithm (HR-POA) for Energy-Efficient Cluster Head Selection in Heterogeneous Wireless Sensor Networks

Abstract

The importance of Wireless Sensor Networks (WSNs) spans multiple areas, notably environmental monitoring, healthcare services, and intelligent urban systems. These networks consist of dispersed sensor nodes that wirelessly exchange data while monitoring environmental or physical conditions. One of the key challenges in Wireless Sensor Networks (WSNs) is the efficient selection of Cluster Heads (CHs) to prolong network lifetime and ensure balanced energy consumption. This study introduces a novel Hybrid Reinforced Pelican Optimization Algorithm (HR-POA), which integrates the Enhanced Pelican Optimization Algorithm (EPOA) with Particle Swarm Optimization (PSO) and Reinforcement Learning (RL) to achieve efficient Cluster Head (CH) selection in heterogeneous wireless sensor networks (HWSNs). HR-POA is a promising solution for energy-efficient clustering since it considerably improves WSN performance by utilizing intelligent routing and a hybrid optimization approach. The proposed algorithm considers node energy, distance, and adaptive Q-learning-based routing to improve energy efficiency and network performance. The effectiveness of HR-POA in comparison to current CH selection algorithms has been assessed through extensive simulated studies. HR-POA demonstrates notable improvements in energy efficiency, network longevity, and packet delivery ratio compared to current CH selection methods, as evidenced by simulation results. By advancing energy-aware clustering approaches in WSNs, the suggested approach opens the door to more intelligent and sustainable wireless sensor networks.