Satellite-Assisted UAV Control: Sensing and Communication Scheduling for Energy Efficient Data Collection

The Internet of Thing (IoT) devices play a vital role in collecting mission-critical and time-sensitive sensing data from remote areas, where traditional terrestrial networks are constrained by sparse infrastructures. However, resource-limited ground devices (GDs) in such scenarios often lack the ability to directly transmit essential information to distant data centers. To overcome this challenge, this paper proposes a Satellite-unmanned aerial vehicle (UAV)-assisted data collection framework, where the UAV is controlled by a remote control center via satellite relays. Aiming to maximize the energy efficiency (EE) of the UAV, we first design a reference trajectory to the UAV with given hovering positions. Subsequently, we optimize the power allocation for communication and state sensing strategies for trajectory tracking control, while guaranteeing control stability and communication reliability. These challenging problems are addressed using sequently an efficient algorithm, incorporating Deep Q-Network (DQN), closed-form derivations, and one-dimensional search method. Extensive numerical simulations and experimental validations are conducted to demonstrate the effectiveness of the proposed approach. Key findings point that the data size of collection has greater impacts than transmission power. Moreover, the results reveal the relationships among the communication, control and state sensing in terms of the EE.