Research On Efficient Collaborative Optimization And Secure Communication Scheme Of Vehicle-Assisted Computing Resources

With the development of the Internet of Vehicles,the number of autonomous vehicles is increasing rapidly,and a series of on-board services,including information interaction,safe driving and communication efficiency,have brought many problems.The massive data generated by on-board services has brought great pressure on the network bandwidth.Among them,computation-intensive and delay-sensitive services pose severe challenges to the storage and computing capacity of the network.Vehicular Edge Computing(VEC)is an advanced computing model that will improve the quality of vehicle communication service by offloading computing tasks onto the VEC server.However,the computing resources of VEC server are actually very limited,which cannot meet the offloading requirements of a large number of computing tasks of vehicles.At the same time,communication security is the key problem of task offloading in VEC scenario.Therefore,this paper will dig deep into the computing resources of idle-parked vehicles on both sides of urban roads,explore efficient collaborative solutions for task offloading and resource allocation,so as to maximize the use of existing idle resources,improve the quality of communication services during offloading peak hours,and improve the security of data during offloading.The main research contents of this paper are as follows:1.This chapter studies the use of idle-parked vehicle assisted VEC server to offload computing tasks,which can not only increase resource capacity,but also expand the communication range.Firstly,this chapter constructs an idle-parked vehicle assisted VEC model,which encourages idle-parked vehicles to assist the VEC server to complete the calculation task by receiving rewards from the VEC server.Secondly,on the basis of comprehensive consideration of the selection strategy and pricing strategy,a more flexible dynamic offloading scheme is studied.Then,based on Stackelberg game,the interaction between VEC servers and idle-parked vehicles is analyzed,and backward induction method is used to prove that the game has a unique Nash equilibrium.Finally,an improved JSPBB algorithm based on Branch and Bound joint selection decision and pricing decision is designed.Under this pricing optimal offloading strategy,the optimal pricing is realized under the condition of maximizing the utility of idle-parked vehicles and each VEC server,which maximizes the utility of the system and makes the efficient collaboration of computing resources.2.The problem of asking vehicle users to worry about data privacy when offloading tasks to VEC is studied in vehicle communication s.Firstly,this chapter studies a VEC model with data security encryption in task offloading,and analyzes the computational offloading and resource allocation problems in the VEC system.The goal is to minimize the computational cost of t he system by measuring the task processing delay and the energy consumption of the requesting vehicle user.Secondly,because Dynamic Voltage Scaling(DVS)is an effective low energy technology with flexibility in VEC systems,this chapter comb ined the technology with task offloading to effectively reduce the system energy consumption.At the same time,AES encryption technology is used to ensure data security during offloading.Finally,an improved IMSO algorithm computational offload for multi-user communication security is proposed to minimize the cost of security task offload.