  Due to flexibility, autonomy and low operational cost, unmanned aerial
vehicles (UAVs), as fixed aerial base stations, are increasingly being used as
\textit{relays} to collect time-sensitive information (i.e., status updates)
from IoT devices and deliver it to the nearby terrestrial base station (TBS),
where the information gets processed. In order to ensure timely delivery of
information to the TBS (from all IoT devices), optimal scheduling of
time-sensitive information over two hop UAV-relayed IoT networks (i.e., IoT
device to the UAV [hop 1], and UAV to the TBS [hop 2]) becomes a critical
challenge. To address this, we propose scheduling policies for Age of
Information (AoI) minimization in such two-hop UAV-relayed IoT networks. To
this end, we present a low-complexity MAF-MAD scheduler, that employs Maximum
AoI First (MAF) policy for sampling of IoT devices at UAV (hop 1) and Maximum
AoI Difference (MAD) policy for updating sampled packets from UAV to the TBS
(hop 2). We show that MAF-MAD is the optimal scheduler under ideal conditions,
i.e., error-free channels and generate-at-will traffic generation at IoT
devices. On the contrary, for realistic conditions, we propose a
Deep-Q-Networks (DQN) based scheduler. Our simulation results show that
DQN-based scheduler outperforms MAF-MAD scheduler and three other baseline
schedulers, i.e., Maximal AoI First (MAF), Round Robin (RR) and Random,
employed at both hops under general conditions when the network is small (with
10's of IoT devices). However, it does not scale well with network size whereas
MAF-MAD outperforms all other schedulers under all considered scenarios for
larger networks.
