  Unmanned aerial vehicle (UAV)-aided data collection is a new and promising
application in many practical scenarios. In this work, we study the age-optimal
trajectory planning problem in UAV-enabled wireless sensor networks, where a
UAV is dispatched to collect data from the ground sensor nodes (SNs). The age
of information (AoI) collected from each SN is characterized by the data
uploading time and the time elapsed since the UAV leaves this SN. We attempt to
design two age-optimal trajectories, referred to as the Max-AoI-optimal and
Ave-AoI-optimal trajectories, respectively. The Max-AoI-optimal trajectory
planning is to minimize the age of the `oldest' sensed information among the
SNs. The Ave-AoI-optimal trajectory planning is to minimize the average AoI of
all the SNs. Then, we show that each age-optimal flight trajectory corresponds
to a shortest Hamiltonian path in the wireless sensor network where the
distance between any two SNs represents the amount of inter-visit time. The
dynamic programming (DP) method and genetic algorithm (GA) are adopted to find
the two different age-optimal trajectories. Simulation results validate the
effectiveness of the proposed methods, and show how the UAV's trajectory is
affected by the two AoI metrics.
