With the widespread use of various types of clean energy sources such as photovoltaic, supported by mobile edge computing technology, UAVs are always used to perform data collection for the outdoor power grid terminals. However, the substantial growing of the number of terminals to be collected and the limited energy and dynamic flight of UAVs, make it is difficult for UAVs to obtain the accurate locations of all terminals quickly. Therefore, this paper designed an optimal UAVassisted mass terminal data collection strategy based on edge computing. This work constructed a location error model based on random distribution, and designed a joint optimization strategy for the UAV flight trajectory and the task transmission of the grid devices to be collected. For the problem solution, it used the Bernstein-type inequalities to relax the variables to eliminate the influence caused by the probability distribution of device locations, and then solved the trajectory planning of the UAV by using continuous convex approximation. The simulation results show that under the proposed strategy, the UAV is closer to the smart grid failure terminal device, and the data collection time is longer and accuracy is higher.

[2023-05-31] Accepted Paper /doc/2023.02.0110/v1

[2023-12-05] Printed Article /doc/2023.02.0110/v2