Abstract

Efficient resource allocation in Wireless Sensor Networks (WSNs) is critical due to the constrained energy resources of sensor nodes and the dynamic nature of network topologies. Traditional clustering and routing algorithms often struggle to maintain energy efficiency and network stability, leading to reduced network lifespan and suboptimal performance. To address these challenges, a Hybrid Chaos-Permutation Evolutionary Algorithm (HCPEA) is proposed, integrating chaotic permutation theory with an adaptive evolutionary framework for energy-efficient clustering and routing. The HCPEA optimizes cluster head selection and transmission paths by leveraging chaotic maps for enhanced population diversity and permutation-based refinements to avoid premature convergence. Simulation results demonstrate that HCPEA significantly outperforms conventional methods, including Particle Swarm Optimization (PSO) and Genetic Algorithm (GA), in terms of energy efficiency, packet delivery ratio, and network lifetime. Compared to GA and PSO, HCPEA achieves an 18.5% improvement in energy efficiency, a 22.3% increase in packet delivery ratio, and a 25.7% extension in network lifetime under varying network scales and dynamic conditions. These findings establish HCPEA as a robust and scalable solution for sustainable WSN operations, ensuring reliable data transmission and prolonged network functionality.

Authors

C. Vimalarani
Karpagam Institute of Technology, India

Keywords

Hybrid Chaos-Permutation, Energy-efficient clustering, Evolutionary Algorithm, Wireless Sensor Networks, Network Lifetime