###### Abstract
Businesses that stay put on the road offer a variety of ITS security services, including freeway management, accident detection and avoidance, and climate control. Businesses typically employ unicast mode for sending sensitive information, and the same is true of the ITS application known as VRC (Vehicle-to-Roadside Communication) that these businesses offer. For example, ITS software could help cars detect and avoid collisions, assist drivers of frame vehicles, and describe their locations. Maintaining a safe distance from potential collisions or taking the necessary precautions is made easier when such vehicles are transported and used responsibly [8]. To enable the correct sending of various forms of security notifications, timely, reliable, and useful information is required. For instance, sending recordings of street conditions (such as heavy traffic, a catastrophic event, or fire) in advance of the route could allow drivers to make complex judgments in advance or reverse course [9]. In addition, many gas stations, weather reports, crisis management offices, intelligent letters, Internet connections, and other services are now integrated into vehicle-based communication and entertainment systems, making for a more pleasant and informative driving experience for everyone. Creating a system that allows direct messaging between cars is impossible without first ensuring a high level of service quality (QoS) [10], which includes its media services [11]. By eliminating the need for roadside equipment and allowing vehicles on the same network to exchange data directly, Vehicle-to-Vehicle (V2V) communication has benefited [12]. Traffic reliability, infotainment network security, and driver and passenger safety are among the primary motivations behind V2x application optimization. It has been established that V2x applications encounter several challenges regarding the appropriateness of decision-making and the consistency and dependability of data sharing across vehicles. Many of these difficulties can be overcome with the help of artificial intelligence (AI) techniques, in particular those that deal with decision-making in IoV systems [13]. By limiting the number of clusters, many bio-inspired routing (clustering) algorithms have been created to guarantee optimal route selection, facilitating fast V2V communication among vehicular networks without the need for centralized infrastructure to extend the life of the network. Delay mitigation, topological stability in networks, bandwidth optimization, and data aggregation are some of the issues investigated in this paper. The existing whale optimization algorithm for clustering was analyzed, and several changes are proposed to make it more effective. These include using probabilistic modeling, increasing the convergence factor to avoid local optima, and making smart adjustments to the algorithm's self-adaptive weights. An intelligent probabilistic whale optimization strategy, inspired by the way whales locate and target prey, has been proposed in [14] as a way to deal with routing problems in the context of cluster deployment. The primary contribution is as follows:  
* A mathematical model of an intelligent whale optimization algorithm (p-WOA) for cluster optimization was developed. * Predictive vehicle initialization procedures to eliminate randomness were created. * Self-adjusted weights of each vehicle based on their fitness functions for optimal performance were designed. * A statistical analysis to evaluate the developed method with other well-established methods was performed.