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Lookup NU author(s): Dr Bo WeiORCiD
This is the authors' accepted manuscript of an article that has been published in its final definitive form by Institute of Electrical and Electronics Engineers Inc., 2021.
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© 1967-2012 IEEE.Recently, Unmanned Aerial Vehicle (UAV) swarm has been increasingly studied to collect data from ground sensors in remote and hostile areas. A key challenge is the joint design of the velocities and data collection schedules of the UAVs, as inadequate velocities and schedules would lead to failed transmissions and buffer overflows of sensors and, in turn, significant packet losses. In this paper, we optimize jointly the velocity controls and data collection schedules of multiple UAVs to minimize data losses, adapting to the battery levels, queue lengths and channel conditions of the ground sensors, and the trajectories of the UAVs. In the absence of the up-to-date knowledge of the ground sensors' states, a Multi-UAV Deep Reinforcement Learning based Scheduling Algorithm (MADRL-SA) is proposed to allow the UAVs to asymptotically minimize the data loss of the system under the outdated knowledge of the network states at individual UAVs. Numerical results demonstrate that the proposed MADRL-SA reduces the packet loss by up to 54% and 46% in the considered simulation setting, as compared to an existing DRL solution with single-UAV and non-learning greedy heuristic, respectively.
Author(s): Emami Y, Wei B, Li K, Ni W, Tovar E
Publication type: Article
Publication status: Published
Journal: IEEE Transactions on Vehicular Technology
Year: 2021
Volume: 70
Issue: 10
Pages: 10986-10998
Print publication date: 01/10/2021
Online publication date: 08/09/2021
Acceptance date: 03/09/2021
Date deposited: 29/06/2023
ISSN (print): 0018-9545
ISSN (electronic): 1939-9359
Publisher: Institute of Electrical and Electronics Engineers Inc.
URL: https://doi.org/10.1109/TVT.2021.3110801
DOI: 10.1109/TVT.2021.3110801
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