Edinburgh Napier University

  Unmanned aerial vehicle (UAV) has been considered a promising technology for advancing terrestrial mobile computing in the dynamic environment. In this research field, throughput, the number of completed tasks and latency are critical evaluation indicators used to measure the efficiency of UAVs in existing studies. In this paper, we transform these metrics to a single optimization objective, i.e., throughput maximization. To maximize the throughput, we consider realizing this goal in two respects. The first is to adapt the formation of the UAVs to provide cooperative computing service in a dynamic environment, we integrate a policy-based gradient algorithm and the task factorization network as a new reinforcement learning algorithm to improve the cooperation of UAVs. The second is to optimize the association process between UAVs and users, where the heterogeneity of tasks is considered. This algorithm is modified from the Gale-Shapley stability concept to optimize the appropriate association between tasks and UAVs in a dynamic time-varying condition to get the near-optimal association with few iterations. The scheduling of dependent tasks and independent tasks jointly also has to be considered. Finally, simulation results demonstrate the improvement of cooperation performance and the practicability of the association process.