Energy and Service-priority aware Trajectory Design for UAV-BSs using Double Q-Learning

Sayed Amir Hoseini; Ayub Bokani; Jahan Hassan; Shavbo Salehi; Salil S. Kanhere

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arXiv · 2010.13346

Energy and Service-priority aware Trajectory Design for UAV-BSs using Double Q-Learning

Sayed Amir Hoseini·Ayub Bokani·Jahan Hassan·Shavbo Salehi·Salil S. Kanhere

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Abstract

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.

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Sayed Amir Hoseini, Ayub Bokani, Jahan Hassan, Shavbo Salehi, Salil S. Kanhere. 2020-10-26. Energy and Service-priority aware Trajectory Design for UAV-BSs using Double Q-Learning. https://arxiv.org/abs/2010.13346

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