Abstract
In this paper, we study the unmanned aerial vehicle (UAV) assisted data collection problem to improve the information freshness in wireless powered Internet of things (IoT) networks. In our system, one UAV collects the sensing information from the sensors timely. To improve the system sustainability, one unmanned vehicles acts as a mobile charging station (MCS) to recharge the sensors wirelessly and replace the battery of the UAV when necessary. We aim to design the trajectory of the UAV and the moving path of the MCS jointly to minimize the average age of information (AoI) collected from the sensors. The problem is formulated as a partially observed Markov decision process (POMDP) with large state and action spaces. To seek the optimal solution, a multi-agent deep reinforcement learning algorithm based on value-decomposition networks (VDN) is developed to make real-time decisions according to partial observations in the environment. Simulation results verify the effectiveness of the proposed algorithm.
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