Intelligent transportation systems (ITS) have advanced significantly over the past years as an incredible technology for averting congested traffic and enhancing traffic safety. Recent researchers show that incorporating Wireless Sensor Networks (WSN) into ITS can decrease the necessary investment and permits the creation of intelligent collaborative applications that enhance traffic efficiency and driver safety. In this paper, we propose a Trust Aware Clustering Technique based on Rat Swarm Optimizer for WSN-based Intelligent Transportation System (TACTIRSO), which is a secure method for selecting cluster heads (CHs) based on nodes’ trust value. We employed the Rat Swarm Optimizer (RSO), one of the most recent swarm-based optimization methods, to efficiently choose CHs. For the selection of CH, the proposed fitness function takes into account the node remaining energy and trust value. Moreover, the exponential moving average model is employed to dynamically change the predefined threshold values according to the network state. In order to enhance the performance of RSO, we applied different local search strategies in addition to an energy and trust-aware method of initializing the rat population. The simulation results reveal that TACTIRSO outperforms existing studies in terms of energy efficiency, selection of most trustworthy nodes, and average network lifetime. Numerical results indicate that TACTIRSO improves average network stability by at least 1.13 times, average trust value by at least 3.31 times, and reliability by at least 4.45 times over other schemes in a heterogeneous network, while these improvements are by 1.02 times, 0.33 times, and 3.52 times, respectively, in a homogeneous network.
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