Task Offloading And Resource Allocation Algorithms For Mobile Edge Computing Based On UAV Communications

Mobile edge computing refers to the processing,analysis,and storage of data close to where it is generated.Traditional mobile edge computing systems install edge computing servers in base stations and wireless access points,so that traditional mobile edge computing systems have a strong dependence on ground infrastructures.Unmanned aerial vehicles(UAVs)are easy to deploy,has strong mobility,and can expand the coverage of wireless networks.In mobile edge computing systems,UAVs can be equipped with edge computing servers to provide dynamic and efficient computing services for ground users.Mobile edge computing systems based on UAV communications hava better performance than traditional fixed mobile edge computing systems.In mobile edge computing systems based on UAV communications,the offloading methods of user computing tasks and the allocation methods of computing and communication resources are the key factors affecting the energy consumption and user service quality.Therefore,it is of great significance to study task offloading and resource allocation algorithms of mobile edge computing based on UAV communications.On the basis of reviewing the research status of task offloading and resource allocation algorithms for mobile edge computing based on UAV communications at home and abroad,this paper designs and verifies the task offloading and resource allocation algorithms for mobile edge computing based on UAV communications.The main work is as follows:1.A task offloading and resource allocation algorithm for mobile edge computing based on single-UAV communications is proposed,and a system model for the singleUAV communications scenario is established.The optimization goal is to minimize the total energy consumption of users.By jointly optimizing the deployment location of the UAV,user computing task offloading ratio,bandwidth allocation,and task offloading duration,the total energy consumption of users is minimized.Simulation results show that the proposed algorithm has good convergence characteristics,and can significantly reduce the total energy consumption of users.2.A task offloading and resource allocation algorithm for mobile edge computing based on multi-UAV communications is proposed,and a system model for the multiUAV communications scenario is established.The optimization goal is to maximize the total amount of computing data for all users.By jointly optimizing the user-UAV allocation,local CPU frequency,user data transmission power,bandwidth allocation,and UAV flight trajectory,the total amount of computing data for all users is maximized.Simulation results show that the proposed algorithm can not only significantly increase the total amount of computing data for all users,but also dynamically adjust the UAV’s flight trajectory according to the user’s distribution location and the UAV’s maximum flight speed.3.A task offloading and resource allocation algorithm for mobile edge computing based on UAV relay communications is proposed,and a system model for the UAV relay communications scenario is established.The optimization goal is to minimize the weighted sum of energy consumption of the UAV and the users.By jointly optimizing the flight trajectory of the UAV,the local computing frequency,the computing frequency allocated by the UAV,the amount of user task offloading data,the user time slot allocation,the amount of UAV relay data,and the UAV relay time slot allocation,the weighted sum of energy consumption of the UAV and the users is minimized.Simulation results show that the proposed algorithm has good convergence characteristics,and can significantly reduce the weighted sum of energy consumption of the UAV and the users.