Abstract: High reliability and low latency is one of the most important research directions in edge computing networks for vehi-cular networking.In order to meet the complex and variable task requests in vehicular networking networks,communication and computation resources are allocated effectively and efficiently.A multi-objective reinforcement learning strategy for intelligent communication and computation resource allocation based on the combination of task queuing theory model and edge computing model is proposed.The strategy combines the allocation of communication and computation resources to reduce the total system cost consisting of latency and reliability.The strategy can be decomposed into three algorithms,firstly,the joint computational offloading and collaboration algorithm is a generic framework for the strategy which first selects the offloading layer for the generated task requests such as the edge computing layer and the local computing layer using the KNN method.Then,when the local computing layer is selected to perform the task,an algorithm called collaborative vehicle selection is used to find the target vehicle to perform the collaborative computation.Finally,the allocation of communication and computational resources is defined as two independent objectives and the algorithm called multi-objective resource allocation uses reinforcement learning at the mobile edge computing layer to achieve an optimal solution to the problem.Simulation results show that the proposed strategy effectively reduces the total cost of the system compared to random computing,all edge computing and all local computing.The KNN approach saves the total cost of the system compared to the random offloading approach and the reinforcement learning algorithm outperforms the traditional particle swarm algorithm in controlling the total cost of the system.

Key words: Internet of Vehicles, Edge computing, Queueing theory, KNN, Resource allocation, Reinforcement learning