Multi-UAV Assisted Computing Task Offloading for Large Scale Users

The rapid advancement of Mobile Edge Computing(MEC) technology has made it an indispensable part of fifth-generation( 5G) network architecture. The flexibility and mobility of Unmanned Aerial Vehicle (UAV) can effectively respond to rapidly changing user requirements in unexpected situations. To deal with the UAV-assisted offloading problem, there are currently two challenges to be solved, which are the command architecture relationship of UAV-assisted offloading and how to balance the number of clustering centers and the energy consumption of UAV s after user clustering. In this study, We propose innovatively improved path planning and user clustering algorithms to address the issue of large-scale user-assisted UAV offloading. We develop the UAV model and User Distribution model, generate UAV-assisted offloading strategies, and conduct a large number of simulation experiments to verify.