Research Of Air-ground Cooperative MEC Task Offloading Strategy

With the digital transformation of various industries,processing a tremendous amount of data information has gradually become an urgent need.Mobile edge computing(MEC)technology can effectively relieve the pressure of information transmission.The technology migrates computing resources from the remote cloud to the nearby network edge.In this way,the latency and energy consumption are significantly reduced.However,geographical conditions can also easily affect the current MEC technology.Especially in emergency scenarios,it is difficult to set up MEC servers,thus the provision of stable communications seems unachievable.Therefore,the researchers propose a concept that uses the unmanned aerial vehicle(UAV)as an aerial edge node to assist the communication of ground users.UAVs have the advantage of flexible deployment over the traditional architecture.With this advantage,UAVs can effectively make up for the deficiency of land MEC architecture in emergency scenarios.In MEC networks,task offloading has become a mainstream method of handling numbers of user services for reducing latency and energy consumption.With the introduction of UAV technology,the complexity of the MEC network has increased significantly.For this reason,an in-depth study of UAV-MEC task offloading method is necessary.Based on the above,this thesis focuses on the air-ground cooperative MEC scenario and investigates how users choose the offloading strategy under different optimization objectives.The specific research content of this thesis shows as follows:Aiming at the air-ground cooperative MEC network,a scenario of UAV relays assisting users to offload tasks to base station is studied.To minimize system delay,the task-offloading ratio and task-offloading strategy are jointly optimized.First,an expression for the offloading ratio based on the minimum delay is given by a mathematical proof in the case of determining policies.Then,the optimization problem is transformed into a task assignment problem and a game theory(GT)model is established.Thus,the set of user task-offloading policies based on the minimum delay is given.Finally,the simulation demonstrates that the system latency is effectively reduced by the above method,which is better than some other common offloading methods in terms of timeliness.Aiming at the air-ground cooperative MEC network,an optimization scheme of UAV relay assisting users’ task-offloading based on minimum system energy consumption is studied.In the proposed scheme,the UAVs act as relays to help the users offload partial tasks to the base station.And the system energy consumption generated is the sum of the energy consumption of the user side and the UAV side.Firstly,with the determined policies,the optimal offloading ratio and optimal transmission power are obtained by iterating over each other based on the constraints of maximum delay and transmission power.The ensemble of user policies based on minimum system energy consumption is then obtained through modeling a game-theoretic approach.Finally,the simulation shows the effectiveness of the proposed algorithm in reducing energy consumption.At the same time,based on the superiority of air-ground collaborative MEC technology,the technology is also suitable for the traffic-intensive vehicular edge computing(VEC)architecture.Therefore,for the scenario of air-ground cooperative VEC task-offloading,the latency and energy consumption in the offloading process are weighed to minimize the system cost.In this scenario,the particle swarm optimization(PSO)algorithm is used to obtain the optimal offloading ratio of each part under the determined strategy.Then the set of vehicular user task-offloading strategy based on the minimum system cost is obtained by the game theory model.Finally,the simulation results show that,the proposed algorithm can effectively reduce the system cost by comparing with other algorithms.