Trust aware clustering approach for the detection of malicious nodes in the WSN

Abstract

Wireless sensor networks (WSNs) are pivotal in a range of applications such as environmental monitoring, healthcare, and defense. However, their decentralized and resource-constrained nature makes them vulnerable to various security threats, particularly from malicious nodes that can disrupt the network’s functionality. To address this issue, this paper proposes a novel trust aware clustering (LEACH) approach integrated with an optimization-based technique for the detection of malicious nodes in WSNs. The proposed model leverages the low-energy adaptive clustering hierarchy (LEACH) protocol for efficient clustering and energy management while incorporating a trust-based mechanism to evaluate the behavior of nodes. Additionally, an optimization algorithm is employed to enhance the accuracy of malicious node detection and improve the overall network performance. The trust model dynamically updates based on node interactions, ensuring that compromised nodes are detected and isolated promptly. Simulation results demonstrate the efficacy of the proposed approach in terms of increased detection accuracy, reduced energy consumption, and prolonged network lifetime, making it a robust solution for securing WSNs against malicious attacks.