Energy-Efficient Resource Allocation in LEO-Assisted UAV Architecture for Internet of Things

The integration of autonomous aerial vehicles (UAVs) and low-Earth orbit (LEO) satellites has become attractive for Internet of Things (IoT) task processing, as it can overcome obstacles in terrestrial network coverage, such as those in oceans or desert areas. However, it lacks a collaborative approach for allocating the communication and computing resources among UAVs and LEO satellites and optimizing the hovering point of UAVs to prolong their endurance. In this article, we investigate energy-efficient resource allocation in LEO-assisted UAV networks for the IoT. A novel optimization algorithm, that jointly IoT tasks’ offloading decision, UAVs’ region selection, hovering point chosen, and communication and computing resource allocation (ORHCC), is proposed to optimize UAV trajectories and hovering points, enhancing endurance and minimizing energy consumption. In particular, the UAVs’ region selection and IoT tasks offloading are under the dueling deep Q-network (DuDQN) framework, the hovering point chosen and communication and computing resource allocation via the convex solution. The results show that the proposed ORHCC reduces 12.5% and 20.76% energy consumption compared with the proximal policy optimization and greedy baseline, respectively.