An Efficient Resource Management Optimization Scheme for Internet of Vehicles in Edge Computing Environment

The contradiction between limited network resources and a large number of user demands in vehicle environment will cause a lot of system delay and energy consumption. To solve the problem, this paper proposes an efficient resource management optimization scheme for Internet of Vehicles in edge computing environment. Firstly, we give a detailed formulation description of communication and computing cost incurred in the resource optimization process. Then, the optimization objective of this paper is clarified by considering the constraints of computing resources, and system delay and energy consumption are considered comprehensively. Secondly, considering dynamic, random, and time-varying characteristics of vehicle network, the optimal resource management scheme of Internet of Vehicles is given by using distributed reinforcement learning algorithm to optimize total system overhead to the greatest extent. Finally, experiments show that when bandwidth = 40 MHz, the total system cost of the proposed algorithm is only 3.502, while that of comparison algorithms is 4.732 and 4.251, respectively. It is proved that the proposed method can effectively reduce the total system overhead.