The wireless industry’s evolution from fourth generation (4G) to fifth generation (5G) will lead to extensive progress in new vehicular network environments, such as crowdsensing, cloud computing, and routing. Vehicular crowdsensing exploits the mobility of vehicles to provide location-based services, whereas vehicular cloud computing is a new hybrid technology that instantly uses vehicular network resources, such as computing, storage, and Internet for decision-making. In this study, novel crowdsensing, cloud computing, and routing algorithms are developed for a next-generation vehicular network, and they are combined into a hybrid scheme. To provide an effective solution toward an appropriate interactive vehicular network process, we focus on the paradigm of learning algorithms and game theory. Based on individual cooperation, our proposed scheme is designed as a triple-plane game model to adapt to the dynamics of a vehicular network system. The primary advantage of our game-based approach is to provide self-adaptability and responsiveness to current network environments. The performance of our hybrid scheme is evaluated and analyzed using simulation experiments in terms of the cloud service success ratio, normalized dissemination throughput, and crowdsensing success probability.
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