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.
@article{arxiv.2010.13346,
title = {Energy and Service-priority aware Trajectory Design for UAV-BSs using Double Q-Learning},
author = {Sayed Amir Hoseini and Ayub Bokani and Jahan Hassan and Shavbo Salehi and Salil S. Kanhere},
journal= {arXiv preprint arXiv:2010.13346},
year = {2020}
}