  Due to the flexibility and low operational cost, dispatching unmanned aerial
vehicles (UAVs) to collect information from distributed sensors is expected to
be a promising solution in Internet of Things (IoT), especially for
time-critical applications. How to maintain the information freshness is a
challenging issue. In this paper, we investigate the fresh data collection
problem in UAV-assisted IoT networks. Particularly, the UAV flies towards the
sensors to collect status update packets within a given duration while
maintaining a non-negative residual energy. We formulate a Markov Decision
Process (MDP) to find the optimal flight trajectory of the UAV and transmission
scheduling of the sensors that minimizes the weighted sum of the age of
information (AoI). A UAV-assisted data collection algorithm based on deep
reinforcement learning (DRL) is further proposed to overcome the curse of
dimensionality. Extensive simulation results demonstrate that the proposed
DRL-based algorithm can significantly reduce the weighted sum of the AoI
compared to other baseline algorithms.
