Unmanned aerial vehicles (UAVs) are now beginning to be deployed for
enhancing the network performance and coverage in wireless communication.
However, due to the limitation of their on-board power and flight time, it is
challenging to obtain an optimal resource allocation scheme for the
UAV-assisted Internet of Things (IoT). In this paper, we design a new
UAV-assisted IoT systems relying on the shortest flight path of the UAVs while
maximising the amount of data collected from IoT devices. Then, a deep
reinforcement learning-based technique is conceived for finding the optimal
trajectory and throughput in a specific coverage area. After training, the UAV
has the ability to autonomously collect all the data from user nodes at a
significant total sum-rate improvement while minimising the associated
resources used. Numerical results are provided to highlight how our techniques
strike a balance between the throughput attained, trajectory, and the time
spent. More explicitly, we characterise the attainable performance in terms of
the UAV trajectory, the expected reward and the total sum-rate.

本文旨在通过设计并运用基于最短路径的无人机来提升物联网设备数据的收集和资源分配。同时，设计了一个基于深度强化学习的技术来获取特定区域内的最优轨迹和吞吐量，从而实现了无人机自主收集所有数据的目标，而且显著提高了总速率，最小化了关联资源的使用。实验结果证实了方案在效率、轨迹、时间等方面的优势。