Research On Task Offloading Strategy And Resource Allocation Schemes In MEC-enabled Internet Of Vehicles

The rapid progress of wireless technology and the Internet of Things has accelerated the development of smart cars,enabling smart vehicles to provide better services to humans.Currently,vehicle terminals have been widely deployed in smart vehicles,and tasks generated by most vehicle applications can be processed by the vehicle terminal portion of the smart vehicle.However,with the emergence of some emerging Internet of Vehicle services,it is difficult for the computing resources provided by vehicle terminals to handle such computing-intensive tasks within the deadline,and the surge in the amount of task data has challenged traditional vehicle networks.In order to solve this problem,Mobile Edge Computing(MEC)is considered as a promising solution,which pushes computing resources to the wireless access network and provides offload services near the vehicle.Due to the short distance,the MEC server paradigm can provide rapid interaction during task offloading and enrich vehicle user experience with delay-sensitive application tasks.The specific research contents of this thesis are as follows:1.Firstly,in order to realize the green communication of vehicle users in the scenario of vehicular networks,this thesis proposes to determine obtain the optimal computing node for each vehicle task based on analyzing all parameters of the current vehicle task,so as to minimize the average energy consumption of the system.In the vehicular networks,on the premise of ensuring that the queue length of each computing node is stable and meeting the task deadline,this thesis combines Lyapunov theory and greedy algorithm to obtain the optimal dynamic offload strategy of the vehicle task.The theoretical analysis and simulation results show that the proposed algorithm in this thesis has lower complexity.Compared with the algorithms of the shortest queue waiting time and the full offloading to MEC,the proposed algorithm can effectively reduce the energy consumption of task execution and greatly improve the offloading efficiency.2.Secondly,in the scenario of multiple MEC systems with dense vehicles,it may be difficult for the MEC server to provide sufficient computing resources.In view of this situation,this thesis investigates a method to compensate for the lack of computing resources in the system by adding a backup MEC server.This thesis combines task offloading strategy and computing resource allocation to minimize system cost.Since the objective function is difficult to solve directly,a heuristic algorithm is employed to solve the problem,and the original problem is converted into a local computing resource allocation sub-problem and a task offloading sub-problem.The local computing resource allocation problem can be solved via mathematical derivations.The task offloading subproblem combines task offloading and MEC resource allocation,which is solved by the Lagrangian optimization algorithm to excute the vehicle tasks with the minimum cost.The theoretical analysis and simulation results show that the proposed scheme in the thesis can greatly reduce the calculation cost of the system.The proposed algorithm is more obvious for reducing the system cost especially when there are more vehicles on the road.