In the edge computing environment of the Internet of Vehicles(IoV),with the aim of efficiently offloading tasks and allocating resources to alleviate the limited storage and computing power of vehicles,this study proposes a offloading algorithm based on multi-agent and double-layer offloading in IoV.A three-layer network model consisting of a Mobile Edge Computing(MEC)server,Vehicles,and Non-Task Vehicle Cloud(MEC-V-NTVC)interconnection is first proposed.Additionally,task,judgment,and calculation models are established.Second,the computational offloading and resource allocation of task vehicles are abstracted into a Partially Observable Markov Decision Process(POMDP),and a double-layer offloading mechanism is proposed to minimize the system cost.Applying a double-layer offloading mechanism and monotonic value function factorization for deep multi-agent reinforcement learning QMIX,a deep reinforcement learning offloading algorithm DLSQMIX based on the double-layer offloading mechanism is proposed,which coordinates task vehicles,non-task vehicles,and state information,considering the time constraint and cooperates with the computation power of the MEC and the non-task vehicle cloud,to learn optimal offloading decisions.The system overhead and latency are compared and explained in terms of the computing power of edge servers and non-task vehicles,number of task vehicles and non-task vehicles,and average task volume.The simulation experiment results demonstrate that the DLSQMIX algorithm can effectively solve the task-offloading problem.Compared with the Genetic Algorithm(GA),Particle Swarm Optimization(PSO)algorithm,and QMIX algorithm,the proposed algorithm reduces the system overhead by 2.52％-3.91％and latency by 3.50％-6.59％.
Internet of Vehicle(IoV)edge computingnon-task vehicle clouddouble-layer offloading mechanismmonotonic value function factorization
维普
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