IoRT Data Collection With LEO Satellite-Assisted and Cache-Enabled UAV: A Deep Reinforcement Learning Approach

Space air ground integrated network (SAGIN), leveraging low earth orbit (LEO) satellites and Unmanned Aerial Vehicles (UAVs), is expected to play a key role in providing services to Internet of Remote Things (IoRT) in the sixth generation (6G) communications. Our considered SAGIN incorporates a cache node on the UAV to cope with the data rate fluctuation in the backhaul link (UAV to satellite), allowing temporary storage of collected data during low data rate periods. In this paper, we aim to minimize the completion time of data collection in SAGIN by optimizing the UAV trajectory, IoRT device association scheme, and data caching policy (whether to store data temporarily or not in the UAV). Since the formulated problem is challenging to solve by using traditional optimization methods due to the unknown number of decision variables and the changing environment, we propose a deep reinforcement learning (DRL)-based algorithm to efficiently solve it. Simulation results demonstrate that our proposed algorithm requires less time to complete data collection compared to both the circular trajectory scheme and the no-cache node scheme under various setups. Moreover, our proposed algorithm can adapt to uneven data distribution by approaching closer to the IoRT nodes with large data sizes, and it can also mitigate the influence of backhaul link fluctuations with the aid of the cache node.