Next generation mobile networks have proposed the integration of Unmanned Aerial Vehicles (UAVs) as aerial base stations (UAV-BS) to serve ground nodes with potentially varying QoS requirements. However, the dependence on the on-board, limited-capacity battery of the UAV-BS limits their service continuity. While conserving energy is important, meeting the QoS requirements of the ground nodes is equally important. We present an energy-efficient trajectory optimization for the UAV-BS while satisfying QoS requirements. We model the trajectory optimization as an MDP problem and solve it using Q-Learning. Simulation results reveal that our proposed algorithm decreases the average energy consumption by nearly 55% compared to a randomly-served algorithm.
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 Greedilyserved algorithm, in terms of reducing the average energy consumption of the UAV-BS as well as the service delay for high priority nodes.
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