AoI Oriented UAV Trajectory Planning in Wireless Powered IoT Networks

In the emerging Internet-of-Things (IoT) paradigm, the freshness of sensory information plays a crucial role in online data-analyzing and application-level decision-making. As the tailor-made performance metric of information freshness, the age of information (AoI) depends on the data transmission efficiency and data update frequency, which are energy demanding for IoT devices with limited battery capacity. To alleviate the energy constraints of low-power IoT devices, we propose an AoI-oriented unmanned aerial vehicle (UAVs)-enabled wireless power transmission scheme, where UAVs are deployed to wirelessly charge IoT devices. With the harvested energy, the devices will upload their fresh information to UAVs. The proposed system aims for sustainable IoT networks with practical device-specific energy limitation, which has been long neglected by existing AoI optimization works. In addition, to explore the influence of dynamic time-varying channels on AoI, a practical line-of-sight (LoS)/NLoS channel model is established to accurately depict the dynamic channel characteristics and precisely capture the efficiency of both data transmission and energy harvesting. To achieve the optimal system-level AoI under dynamic channel conditions, a novel deep reinforcement learning-based proactive UAV trajectory planning (PUTP) algorithm is proposed to automatically adjust the UAV fight policy according to the channel variations and the trade-off between the energy transmission and data collection. Extensive simulation results demonstrate that the proposed PUPT algorithm can significantly reduce the AoI by approximately 20% to 65% compared to three other existing trajectory planning algorithms.