  Unmanned aerial vehicles (UAVs) have been proposed as a promising technology
to collect data from IoT devices and relay it to the network. In this work, we
are interested in scenarios where the data is updated periodically, and the
collected updates are time-sensitive. In particular, the data updates may lose
their value if they are not collected and analyzed timely. To maximize the data
freshness, we optimize a new performance metric, namely the Age-of-Updates
(AoU). Our objective is to carefully schedule the UAVs hovering positions and
the users' association so that the AoU is minimized. Unlike existing works
where the association parameters are considered as binary variables, we assume
that devices send their updates according to a probability distribution. As a
consequence, instead of optimizing a deterministic objective function, the
objective function is replaced by an expectation over the probability
distribution. The expected AoU is therefore optimized under quality of service
and energy constraints. The original problem being non-convex, we propose an
equivalent convex optimization that we solve using an interior-point method.
Our simulation results show the performance of the proposed approach against a
binary association.
