Research On Task Offloading And Resource Allocation Methods For Internet Of Vehicles Based On Mobile Edge Computing

The rapid development of Internet of Vehicle(Io V)technology has spawned a large number of computation-intensive and delay-sensitive vehicle applications.The limited computation resources of vehicle terminals cannot meet the latency and energy consumption requirements of emerging applications.The traditional Mobile Cloud Computing-based task offloading has the defects of long time-consuming task transmission and large fluctuation of transmission interference.The emergence of the Io V based on Mobile Edge Computing(MEC),i.e.Vehicular Edge Computing(VEC)network,provides a new paradigm for solving the above contradictions.The edge server is deployed in the Road Side Unit(RSU)near the vehicle to provide computational offloading services.It relieves the calculation pressure of the vehicle,meets the expansion requirements of vehicle computation capabilities and reduces task offloading latency and energy consumption effectively.However,in the VEC network,the computation resources of the edge server are limited and scattered.The low-latency requirements of vehicle applications and the rapid network topology change caused by the high-speed movement of the vehicle will bring severe challenges to the task offloading and resource allocation of the VEC networks.Therefore,this thesis focuses on method of task offloading and resource allocation in VEC networks.The main research contents of the thesis are as follows:To address the problems caused by the scarcity of computation resources and the single pricing mechanism of vehicle edge computation servers,a task offloading method based on the multi-leader and multi-follower Stackelberg game is proposed with comprehensive consideration of the offloading strategy and the pricing strategy.First,vehicles apply partial offloading scheme which alleviates the computational overload problem caused by insufficient computation resources.Second,the utility maximization of vehicles and edge servers are modeled as multileader and multi-follower Stackelberg game to optimize the utility of the both under the delay constraint.The differentiated pricing mechanism is used to assist the vehicle to offload.Finally,the distributed iterative algorithm is used to analyze and achieve the game equilibrium,and achieve the optimal offloading and price strategy.Extensive experiments show that under different network conditions,the proposed offloading scheme converges fast and always outperforms other schemes in terms of vehicle utility.To address the changeable topological environment caused by the rapid movement of vehicles in the VEC networks and unreasonable resource allocation problem caused by the limited computation resources of edge servers,a task offloading and resource allocation method based on matching theory and price incentives is proposed.First,two types of vehicles,task vehicles and service vehicles,are defined.An offloading mode selection mechanism of task vehicle is proposed.Second,a cost function model of the vehicle is established with comprehensively considering the offloading delay,energy consumption,cost and transmission gain of the vehicle.The transmission gain is due to the mobility of the vehicle.The offloading efficiency and the utilization efficiency of computation resources are improved by minimizing the cost function.Finally,a distributed iterative algorithm is proposed based on matching theory and Lagrange dual method to obtain the optimal offloading strategy and resource allocation strategy.The simulation results show that,compared with other schemes,the algorithm has low complexity and strong robustness,and significantly reduces the cost of vehicles.