Secure Clustering Approach based on Particle Swarm Optimization Algorithm in Wireless Sensor Networks

Abstract

Recently, Wireless Sensor Networks (WSNs) are increasingly utilized across various applications, serving as alarm detectors and sensors. Several clustering algorithms have been developed to enhance WSN energy efficiency and prolong network lifespan. These algorithms typically prioritize energy conservation without addressing security concerns. In clustering topologies, the cluster head (CH) holds a pivotal role, necessitating the selection of reliable and trustworthy CHs to extend network longevity. Trust management emerges as a critical security measure, identifying malicious nodes based on their behavior within the network. This study proposes a clustering algorithm leveraging Particle Swarm Optimization (PSO) to integrate both security and energy considerations during the CH selection process. The algorithm incorporates a trust mechanism to ensure that selected CHs are devoid of malicious intent. It facilitates cluster formation and CH selection, extensively evaluated through experiments comparing its performance with existing algorithms. The findings demonstrate that the PSO-based approach effectively selects trustworthy CHs while mitigating the influence of malicious nodes.