Enhancing Sustainable Data Collection with Unmanned Aerial Vehicles: A Deep Reinforcement Learning Approach

Incorporating unmanned aerial vehicles (UAVs) and distributed sensors into aerial-terrestrial networks (ATNs) improves the ability to monitor remote and challenging environments. However, in such harsh environments, the UAV must return to the base station multiple times to be recharged to ensure that all sensors can be accessed. Neglecting the energy consumption associated with the backhaul recharging path would lead to a decrease in task efficiency and an increase in overall energy consumption. To address the challenge, this paper formulates a UAV back-haul trajectory planning (UBTP) problem, aiming to optimize both the data collection and charging paths for UAVs. At the same time, a two-stage deep reinforcement learning (DRL)-based method is developed to address the UBTP problem. The first stage of the proposed method determines the navigation sequence taking into account the energy consumption of the data collection and charging path. The subsequent stage refines the trajectory by considering the sensing ranges of the sensors. The numerical results demonstrate that the proposed approach provides solutions that are more energy-efficient compared to benchmark methods, thereby enhancing the UAV’s ability to harvest data sustainably and effectively from distributed sensors in challenging environments.