2020/10/26 by Sayed Amir Hoseini, Hoseini, Sayed Amir, Ayub Bokani +7
Computer Science · Engineering · #Artificial Intelligence (cs.AI) #FOS: Computer and information sciences #Networking and Internet Architecture (cs.NI) #Robotic Path Planning Algorithms #Smart Parking Systems Research #UAV Applications and Optimization #cs.AI #cs.NI
paper · pdf · doi:10.48550/arxiv.2010.13346
arxiv created 2020/10/26 · openalex publication_date 2020/10/26 · arxiv updated 2020/10/27 · openalex created_date 2022/07/25 · openalex updated_date 2026/07/28
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.