Research On Task Unloading Mechanism Of Internet Of Vehicles For Delay And Energy Consumption Optimization

Since the beginning of the 21 st century,the number of vehicles has increased dramatically,and a large number of new on-board applications such as on-board multimedia,cruise system and automatic driving have emerged.These applications produce a large amount of data in the use process,which puts forward higher requirements for the computing power and processing delay of the Internet of vehicles.In the Internet of vehicles,the computing power of vehicles is limited,which is not enough to support the computing processing of these applications.In this scenario,the Internet of vehicles based on cloud computing arises at the historic moment.Because the cloud computing deployment location is relatively far away from the vehicle,it can only solve the computing problems in the above requirements,and can’t meet the delay requirements,resulting in the vehicle quality of service(Qo S)has not been significantly improved.The mobile edge computing(MEC)server is often deployed on the roadside unit(RSU)or 5g(5th generation,5g)base station,which can make up for the high delay of cloud computing.In the Internet of vehicles based on MEC,when the vehicle unloads its own computing tasks,the vehicle’s own task size,task priority,available resources of roadside infrastructure and whether the roadside infrastructure is damaged or not will affect the selection of vehicle computing unloading objects.Improper selection of unloading objects will lead to the increase of vehicle energy consumption,cost and delay.To solve the problem of unloading object selection,this paper proposes the following two strategies.(1)In urban traffic,the roadside infrastructure is generally in good condition,and vehicles can unload their own computing tasks from the roadside infrastructure.In this paper,a MEC server selection strategy based on vehicle task priority is proposed.In the problem modeling,this strategy is based on 802.11 p communication protocol,and introduces task priority.In the process of vehicle queuing to unload computing tasks to RSU,the optimal auction mechanism is adopted to achieve Nash equilibrium between vehicle revenue and RSU revenue,so that vehicles can choose the most suitable unloading object.Simulation results show that the scheme can reduce the total cost,total delay and total energy consumption of task unloading on the basis of ensuring vehicle Qo S,and meet multiple performance indicators.(2)When vehicles are driving on mountain roads,the roadside infrastructure is damaged due to natural disasters and people are trapped,users can not communicate with the outside world through the roadside infrastructure,and unmanned aerial vehicle(UAV)can temporarily act as MEC server due to its flexible deployment ability and mobility.This paper proposes a computing rate maximization scheme based on UAV weighted energy minimization.In this scheme,UAV has the ability of wireless power supply,and the energy consumed by users comes from the energy collected by the device.Through the joint optimization of UAV trajectory,CPU frequency and transmit power,the weighted sum of user and UAV energy consumption is optimized.Under the given trajectory,the optimal CPU frequency and transmit power of user equipment in partial unload mode and binary mode are solved,and the optimal unload strategy of user computing task in binary mode is obtained.Simulation results show that the resource allocation scheme is better and converges faster.