Trust Aware Moth Flame Optimization based Secure Clustering for Wireless Sensor Networks

Abstract

Wireless sensor networks (WSN) encompass numerous sensor nodes deployed in the physical environment to sense parameters and transmit to the base station (BS). Since the nodes in WSN communicate via a wireless channel, security remains a significant issue that needs to be resolved. The choice of cluster heads (CHs) is critical to achieving secure data transmission in WSN. In this aspect, this article presents a novel trust-aware mothflame optimization-based secure clustering (TAMFO-SC) technique for WSN. The goal of the TAMFO-SC technique is to determine the trust level of the nodes and determine the secure CHs. The proposed TAMFO-SC technique initially determines the nodes' trust level, and the node with maximum trust factor can be chosen as CHs. In addition, the TAMFO-SC technique derives a fitness function using two parameters, namely residual energy and trust level. The inclusion of trust level in the CH selection process helps to accomplish security in WSN. A comprehensive experimental analysis exhibits the promising performance of the TAMFO-SC technique over the other compared methods.