Optimized Secure Clustering Scheme for Wireless Sensor Networks

Abstract

Wireless Sensor Networks (WSNs) are increasingly employed in various applications, that require efficient, energy-saving and secure transmitting mechanisms. Clustering is a key technique in WSN to enhance scalability, reduce energy consumption, and manage communication. However, tradi-tional clustering algorithms often lack adaptability to alterable wireless environments. This study presents an optimized secure clustering scheme for Wireless Sensor Networks that employs a fuzzy inference system to perform adaptive and secure cluster head selection. The proposed scheme is designed for WSN with a static topology, where sensor nodes remain fixed after de-ployment. This assumption matches Internet of Medical Things (IoMT) applications, such as patient monitoring systems and smart hospital infrastructure, where sensors operate in fixed locations. The proposed fuzzy inference system evaluates parameters of sensor nodes to select optimal cluster heads while mitigating malicious node participation. Next, a genetic algorithm was applied to optimize the parameters of the fuzzy inference system, including the membership function shapes and rule base indices, in order to enhance the accuracy of the proposed clustering scheme. MATLAB simulations demonstrate that the proposed scheme effectively identifies compromised sensor nodes and prevents them from assuming the cluster head role. The security validation confirms that the scheme achieves a high detection rate against internal attacks while extending overall network lifetime and maintaining the low latency. This study provides a feasible solution for secure and intelligent clustering in resource- constrained WSN.