Research On Revenue And Energy Consumption Optimization Based On UAV Assisted MEC

5G(the fifth generation mobile communication)network is officially commercial in China in June 2019.5G network has three characteristics: high speed,low latency and wide connection.Among them,mobile edge computing(MEC)is one of the key technologies,which can meet the needs of users in low latency,low energy consumption,privacy and security.But at present,MEC servers are deployed in fixed locations,so they can not be deployed and provide services quickly when other areas need them.Besides,natural disasters such as earthquake and flood will lead to the destruction of base station(BS)and access point(AP)and make it impossible to communicate with users.Unmanned aerial vehicle(UAV)equipped with communication equipment and MEC equipment can flexibly,quickly and efficiently reach the target area,providing users with computing unloading,data processing and communication relay services,thus reducing the energy consumption and delay of user equipment.Moreover,the energy supply of IOT devices in edge areas is inconvenient,so wireless charging technology can be used to provide energy supplement for ground nodes.However,UAV has short endurance and limited load capacity,which can only provide users with lightweight MEC services.Moreover,UAV aided MEC system also has flight path planning,mission delay and energy consumption optimization problems to be studied.This paper studies how to reduce the delay and energy consumption of user tasks,and improve the benefits of UAV assisted MEC in the case of limited computing resources.It also studies how to reduce the total energy consumption of users and UAVs to improve the endurance of UAV and user equipment.Therefore,the specific research contents and scenarios are summarized as follows:(1)In the third chapter,the UAV deployed in this area can be used as a computing server to help the user equipment(UES)compute tasks,and then return the results to the user.Firstly,the hovering position and altitude of UAV are determined in the service area;Then,the resource is priced in the case of limited computing resources,and the upper and lower limits of the offload mobility are determined by both the delay constraint and the energy consumption constraint.Finally,the offload mobility of each user and the final computing resource pricing are determined iteratively by using the Stackelberg game.Simulation results show that,compared with the original method,the improved algorithm has a significant improvement in MEC revenue and total system revenue,while reducing the average delay and average energy consumption of user tasks.(2)In the third chapter,although the benefits of MEC are improved and the delay and energy consumption of tasks are reduced,the computing capacity of MEC carried by UAV is limited,which can not handle a large number of user tasks.At the same time,the energy carried by UAV is limited,and the endurance time is limited.Therefore,the base station is introduced to solve the problem of limited computing resources of MEC and energy consumption of UAV,This paper studies how to minimize the energy consumption of users and UAVs in the three-tier computing offload of base station UAV user.In the fourth chapter,dynamic voltage and frequency scaling(DVFS)technology is used to optimize CPU computing frequency;The optimal unloading ratio of tasks is obtained by convex optimization theory;Genetic algorithm is used to optimize the hovering time of UAV.The simulation results show that the total energy consumption of UAV and users is reduced and the endurance of UAV is improved by optimizing the task partition,calculation frequency and task execution sequence of three-layer network edge computing.