Energy and Service-priority aware Trajectory Design for UAV-BSs using Double Q-Learning
This work addresses energy efficiency and prioritized service delivery in UAV-assisted IoT networks, representing an incremental improvement over existing methods.
The paper tackles the problem of optimizing UAV base station trajectories to balance energy consumption and service priorities for IoT nodes, using Double Q-Learning to reduce average energy consumption and service delay for high-priority nodes compared to a greedy benchmark.
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.