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In this paper, we give the focus on the continuous advancement in the domain of Vehicular ad hoc networks (VANET’s) and that is developed as a tool for developing the base for platform intelligent mode in the communication (inter vehicle communications) and needful for enhanced improvement under the performance effect and traffic safety. Here, we have deployed the game theory method in the VANET domain and thereafter categorizes between the hop vehicle and the source vehicle. The New approach to the VANET analysis we have identified and used the social parameters and hence made it look more novel approach."Researching vehicle ad hoc networks poses challenges due to their dynamic nature, the movement of large vehicles, unlimited energy resources, and the evolution of wireless networks. Game theory is commonly employed in wireless networks to explore the interplay between competition and cooperation. In this study, we present a system for ad hoc networks in cars, utilizing game theory to automate car groups and board elections. This approach eliminates the necessity for regular bulk updates. Furthermore, the social behavior of each car is leveraged to create clusters in the car environment. The K-means algorithm in machine learning is applied to develop social cars. The proposed system underwent testing for various characteristics, including CH lifetime, average group lifetime, average number of joins, throughput, and packet loss rate. The results demonstrate that VANET performs exceptionally well, achieving an overall performance ranging from 0.95 to 0.989."
Tiwari et al. (Fri,) studied this question.