Two-Tier Intelligent Optimization Framework for Trust-Aware Clustering and Energy-Efficient Routing in VANET Communication

Vehicular Ad hoc Network (VANET) is a currently developing trend that inspires the delivery of several service providers in urban regions. In VANETs, the vehicles signify the nodes in the Network, which want to assure superior assistance when there is a greater node density. In this framework, the combination of probable clustering techniques has the probability of enhancing the safety of the road and easing a trustworthy choice of encouraging message route. The clustering protocols were defined as the perfect candidate for resolving network scalability issues and ensuring reliable data propagation. This study proposes a Two-Tier Intelligent Optimization Framework for Trust-Aware Clustering and Energy-Efficient Routing (TTIO-TACEER) model in VANET. The main purpose of the TTIO-TACEER model is to integrate an optimized clustering mechanism with an efficient routing strategy to enhance network performance and reliability. In the clustering phase, the TTIO-TACEER technique is applied using the Goose Algorithm (GO) to select optimal Cluster Heads (CHs). Furthermore, the multi-criteria Fitness Function (FF) considers Residual Energy (RE), Trust Level (TL), Degree Difference (DD), Total Energy Consumed (TEC), and mobility for cluster formation. TTIO-TACEER is implemented using the Ninja Optimizer Algorithm (NiOA) to optimize routing paths between CHs and the Base Station (BS) in the routing phase. Moreover, the routing FF considers the distance to the BS to improve data transmission reliability while reducing latency (LAT). An extensive simulation validation is executed to highlight the significance of the TTIO-TACEER technique. A brief comparative study described the superior results of the TTIO-TACEER technique when compared to other existing models.