Research On Computation Offloading And Benefit Optimization Based On Vehicular Cooperation For Internet Of Vehicles

In recent years,with the rapid development of wireless mobile communication technology and the Internet of Vehicles(Internet of Vehicles),many new applications have emerged,bringing a lot of convenience to people’s transportation.These new applications often have requirements such as high transmission rate,low latency,and a large amount of computing processing,which occupy a large amount of computing and storage resources of the on-board unit(OBU),and also improve the quality of service of the network.big challenge.Therefore,researchers propose to combine computing offloading technology with the Internet of Vehicles,namely Vehicle Edge Computing(VEC)technology,which has received extensive attention in the industry.Faced with the computing-intensive and delay-sensitive characteristics of the new Internet of Vehicles business,by using the in-vehicle edge computing technology,the computing tasks are migrated from the vehicle to other cooperative computing nodes,and the idle computing and communication resources in the network can be fully utilized.above problem.However,with the increasing number of intelligent vehicles,various resources in the network are relatively limited.How to allocate resources reasonably to improve the utilization of system resources is one of the core problems to be solved at present.In addition,while improving the enthusiasm of nodes to share idle resources,attention should also be paid to controlling the user’s computing offloading service overhead.How to deal with the balance between the two and optimize the benefits of both parties is also a problem worthy of further study.This paper discusses in-depth computing task offloading and resource allocation in in-vehicle edge computing,and designs an effective incentive scheme to promote cooperative computing nodes to share idle resources and optimize the benefits of service requesters and service providers.The main research results of this paper are as follows:Aiming at the benefit optimization problem of cooperative computing offloading among vehicles in the Internet of Vehicles,a vehicle cooperative computing offloading strategy based on Stackberg game is proposed.First,the problem is modeled as a single-leader-multi-follower Stackerberg game model by discussing the interaction between cooperative nodes in the vehicle group that allocate resources and user vehicles requesting unloading.’s uninstall preference rules.Secondly,it is proved that there is a Nash equilibrium solution to this game problem,and a three-point search algorithm is used to solve it.Finally,computing offloading and vehicle group computing resource allocation strategies are designed to minimize the cost of user vehicles and improve the benefits of cooperative nodes under the constraints of delay constraints.The simulation results show that the proposed strategy has good performance in optimizing the benefits of both sides of the game.Aiming at the benefit optimization problem of computing resource auction in the Internet of Vehicles,a computing resource auction and benefit optimization strategy based on matching model is proposed.First,an auction model for IoV computing resources is established around two types of vehicle nodes in the network that have computing offloading requirements and can provide idle computing resources,where the former is the buyer and the latter is the seller.By discussing the interaction between buyers and sellers in the auction market,The computational resource auction problem is modeled as a matching model,and a reputation-based fair auction rule is designed.Second,the stability of this matching process is proved and solved using the delayed acceptance algorithm.Finally,a computing resource auction matching strategy based on the delay and energy consumption utility of both parties is designed,which realizes the optimization of the allocation strategy of computing resources in the auction market and effectively improves the utilization rate of system resources.Simulation results show that the proposed strategy has good performance in optimizing buyer and seller benefits.