A noncooperative game-theoretic approach applied to the service selection in the vehicular cloud

Recent advancements in the automotive area have aided in the development of smart vehicles with high processing, computing, and storage capabilities. These resources are leveraged to cope with the demands of consumer vehicles such as data analysis, optimization, and decision-making. The first and foremost step in establishing this paradigm of computing so-called vehicular cloud is the service selection. Nonetheless, vehicles' high mobility and the network's rapidly changing topology have addressed new challenges such as instability of resources, making their management complicated. Therefore, the vehicular cloud requires robust and efficient resource management solutions to be developed. In this article, we present a game theory-based framework for vehicular resource selection where provider vehicles and consumer vehicles play a game against each other until reach equilibrium in order to enable consumer vehicles to select the most suitable provider vehicles that meet their requirements in terms of service quality and cost. Furthermore, a thorough study was carried out in order to study the interaction between players and predict their future behaviors. As this interaction will result in a pure and mixed equilibrium state, the convergence of the proposed theoretic model for both equilibrium states is rigorously demonstrated under certain assumptions. Effective search algorithms based on the best responses are designed to compute the pure and mixed Nash equilibrium. The suggested approach's verification is evaluated based on the satisfaction of the players and has shown better performance comparing previous algorithms.