Priority Scheduling Strategy for Reduced Energy Consumption in UAV-Aided 6G Green Mobile Edge Computing

With the gradual development of unmanned aerial vehicles (UAV) related disciplines such as mechanics, electronics and wireless communication. UAV technology is an important direction for the future development of sixth generation mobile communications (6G) technology. When the BS computing capacity is insufficient or unavailable, UAV-aided 6G mobile edge computing (MEC) is considered a practical solution to maintain the balance between the number of computing tasks and the number of base stations (BS). Firstly, we consider the transmission power setting and design a prioritization mechanism to determine the link selection. Secondly, we construct energy consumption models for user devices (UDs), BS and UAVs separately. Finally, we propose UAV-aided MEC Genetic Algorithm (UMGA) based on genetic algorithm (GA) to minimize the overall energy consumption of the UAV-aided MEC system. Simulation results show that the system energy consumption of our proposed scheme is lower than the system energy consumption of other baseline schemes. Compared with the three baseline algorithms, the performance of our algorithm is improved by 660%, 300% and 41.2%, respectively.