AoI-minimization in UAV-assisted IoT Network with Massive Devices

The Unmanned aerial vehicle (UAV) assisted Internet of Things (IoT) has attracted substantial attention as it is capable of collecting scattered data to meet the stringent demands of emerging IoT applications. Dispatching UAV to collect data from IoT devices (IoTDs) can significantly improve data freshness, which can be measured by Age of Information (AoI). On the other hand, the quantity of IoTDs increases and existing UAV navigation algorithms for dozens of IoTDs can not be applied to massive IoTDs scenarios directly. In this paper, we investigate the AoI minimization problem in massive IoTDs scenarios. Considering unknown traffic patterns of IoTDs, we reformulate the AoI minimization problem as a Markov decision process (MDP). Then we propose a twin delayed deep deterministic policy gradient (TD3) based UAV navigation algorithm to minimize the average AoI of data collected from IoTDs. Simulation results demonstrate that the proposed algorithm can significantly reduce the average AoI in massive IoTDs scenarios when compared with baseline algorithms.