Fuzzy optimization trust aware clustering approach for the detection of malicious node in the wireless sensor networks

Abstract

Wireless sensor networks (WSNs) are pivotal in various applications, ranging from environmental monitoring to military operations. However, their susceptibility to security threats, particularly from malicious nodes, poses significant challenges to network integrity and data reliability. This paper proposes an innovative methodology that integrates clustering with an optimization approach to effectively identify and mitigate malicious nodes in WSNs. In the proposed methodology, the network is divided into clusters, each managed by a cluster head responsible for monitoring the behavior of nodes within its cluster. Trust values are assigned to nodes based on parameters such as data forwarding accuracy, communication consistency, and energy consumption. These trust metrics are optimized using a sophisticated optimization algorithm, which fine-tunes the decision-making process for identifying malicious nodes. By leveraging clustering, the method efficiently distributes computational tasks, while the optimization algorithm enhances the accuracy of malicious node detection by dynamically adjusting trust thresholds. The approach not only reduces the incidence of false positives but also extends the network lifetime by preventing compromised nodes from disrupting network operations. This trust-aware, optimized clustering strategy offers a robust solution for securing WSNs in critical applications, ensuring reliable and secure data transmission across the network.