VANETs (vehicular ad hoc networks) have evolved as a platform for enabling intelligent inter-vehicle communication while also improving traffic safety and performance. VANETs are a difficult research topic because of the road dynamics, high mobility of cars, their unlimited power supply, and the growth of roadside wireless infrastructures. In wireless networks, game theory approaches have been widely used to investigate the interactions between competitive and cooperative behavior. In this research, we propose a technique for vehicular ad hoc networks that uses a game theory approach to automate vehicle grouping and cluster head nomination. This will eliminate the need for cluster reformation on a regular basis. Furthermore, each vehicle’s social behavior will be exploited to establish clusters in the vehicular environment. For the development of clusters on the social behavior of the cars, a machine learning approach (K-means algorithm) is applied. The proposed system is tested against a variety of characteristics, including CH life time, average cluster member life time, average number of reaffiliation times, throughput, and packet loss rate, and the results indicate that the VANET performed very well with high accuracy in validation and testing, and overall in the range of 0.97 to 0.99.
Recently, extensive research has focused on the design of clustering algorithms for grouping vehicles in a set of clusters in Vehicular Ad Hoc Networks (VANETs). However, due to the dynamic nature of VANETS, frequently connected or detached nodes threaten the stability of the network. When these nodes are clustered heads (CHs), the impact of these disruptions on network performance is even worse. Therefore, the stability of clusters is an important issue that should be maintained to enhance the performance of the network with minimum data loss. In this article, a novel clustering approach is proposed, which is based on the average weight concept of three main attributes, namely, the geostationary coordinates (x, y) and drop density. The average of the above-mentioned attributes is calculated and compared with the values of other vehicles’ attributes. Those vehicles having minimum average values are dropped and not to be considered in the cluster formation. The cluster members having minimum Euclidean distance are considered as Cluster Head (CH) until a certain checkpoint is not attained. The checkpoints are created in order to generate the social score of the vehicles. Once the social score is attained, a multiobjective framework is developed, which considers minimizing the Euclidian distance and maximizing the social score. The social score is generated based on the Quality of Service (QoS) attained attributes, and the data transmission is performed using Ad Hoc On-Demand Distance Vector (ADOV) routing protocol. A comparison of the proposed work and existing work has also been undertaken to demonstrate the improvement in the proposed work. As a result, throughput, PDR, network longevity, and energy consumption have been improved by 28.27%, 20.9%, 23.12%, and 27.48 percent, respectively. Extensive simulations show that the proposed scheme, unlike other popular clustering algorithms, can significantly increase the stability of the network by extending the life of the cluster head.
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