Abstract: Aiming at the problem that the communication interruptions between vehicles and base stations was not fully considered in current vehicular networks with edge computing, and  the resource wastage caused by offloading tasks to a single node for computing, we proposed a vehicular network resource optimization scheme based on the simulated annealing algorithm (SAA). Taking into account the communication retention time and task offloading computation efficiency, we modelled the problem of minimizing the system utility by balancing system latency and energy consumption, and then decomposed it into subproblems of resource allocation and task offloading ratio optimization to solve. Firstly, the resource allocation subproblem was solved by using the Lagrange multiplier method and quasi-convex optimization techniques. Secondly, the SAA was used to jointly optimize the task offloading ratio and resource allocation subproblems. Simulation experiment results show that the proposed scheme can effectively reduce  average latency, energy consumption, and utility of the system, and has good convergence, which  can solve  the problems of latency sensitivity, high energy consumption, and high system overhead in vehicular networks for various applications.

Key words: vehicular networks (V2X), resource allocation, joint optimization, simulated annealing algorithm (SAA)