  Next-generation mobile networks have proposed the integration of Unmanned
Aerial Vehicles (UAVs) as aerial base stations (UAV-BS) to serve ground nodes.
Despite having advantages of using UAV-BSs, their dependence on the on-board,
limited-capacity battery hinders their service continuity. Shorter trajectories
can save flying energy, however, UAV-BSs must also serve nodes based on their
service priority since nodes' service requirements are not always the same. In
this paper, we present an energy-efficient trajectory optimization for a UAV
assisted IoT system in which the UAV-BS considers the IoT nodes' service
priorities in making its movement decisions. We solve the trajectory
optimization problem using Double Q-Learning algorithm. Simulation results
reveal that the Q-Learning based optimized trajectory outperforms a benchmark
algorithm, namely Greedily-served algorithm, in terms of reducing the average
energy consumption of the UAV-BS as well as the service delay for high priority
nodes.
