Task Allocation And Path Planning Based On UAV-Assisted Mobile Edge Computing

With the rapid development of mobile terminals,and the rapid popularization of new applications,the data traffic increase continuously,and Cloud Computing technology is facing enormous challenges due to network delays.As one of the most important technologies in 5G,Mobile Edge Computing(MEC)can provide users with services such as computing,storage,wireless sensing,and communication at the edge of the network,and migrate some tasks from mobile terminals to the MEC server,aiming to reduce latency and the energy consumption of terminal equipment.However,Mobile Edge Computing also faces many technical challenges.On the one hand,Mobile Edge Computing technology is vulnerable to propagation delay and path loss;on the other hand,when infrastructure is damaged,especially in emergency scenarios,Mobile Edge Computing cannot play an effective role.UAV-assisted MEC can better meet the above-mentioned challenges,and UAVs equipped with edge servers can provide services for mobile terminals in emergency scenarios.This thesis has studied the content on the task allocation and path planning based on UAV-assisted Mobile Edge Computing.The main work of this thesis is as follows:1.In terms of task allocation,this thesis study collaborative task allocation based on UAV-Assisted Mobile Edge Computing.Aiming at this problem,the coalition formation game theory is used for modeling.Based on the analysis of the delay and energy consumption of the system communication process and calculation process,the coalition utility function and return function are designed,and the coalition partition formation algorithm is proposed based on the hedonic game.Finally,through numerical simulation experiments,the results prove that compared with other typical strategies,the strategy proposed in this thesis has good time performance and energy consumption requirements.2.In terms of path planning,the A* algorithm is often used to settle the path planning problem of UAVs.However,the flight cost of the UAV under the traditional A* algorithm is relatively high,it takes a long time,the single factor is considered in the design of the cost function,and the planned path is not smooth enough.In order to reduce the computational cost,this thesis proposes an improved A* algorithm.Specific measures include designing oriented cost function and smoothing the planned flight trajectory,thereby reducing search time and optimizing the length of the flight trajectory.Finally,through a large number of numerical simulation experiments,the advantages of the improved A* algorithm in this paper are verified from the two dimensions of search time and path length,and the validity of the proposed algorithm and the correctness of the relevant theoretical conclusions are verified.In summary,this thesis conducts in-depth research on task allocation and collaboration based on UAV-assisted MEC,proposes a task allocation strategy to meet emergency scenarios,and uses coalition formation game theory for modeling.Aiming at the problem of path planning,improve the traditional A* algorithm,which reduces the search time to a certain extent and optimizes the length of the flight trajectory.