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Orchestrated Scheduling and Multi-Agent Deep Reinforcement Learning for Cloud-Assisted Multi-UAV Charging Systems
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Publication Year
2021-06-01
Journal
IEEE Transactions on Vehicular Technology
Publisher
Institute of Electrical and Electronics Engineers Inc.
Citation
IEEE Transactions on Vehicular Technology, Vol.70 No.6, pp.5362-5377
Keyword
Charging schedulingconvex optimizationmulti-Agent deep reinforcement learningunmanned aerial vehicles (UAVs)
Mesh Keyword
Charging energiesCharging systemsCooperative energiesEnergy storage systemsIntelligent energiesManagement frameworksNonconvex problemOptimal solutions
All Science Classification Codes (ASJC)
Automotive EngineeringAerospace EngineeringElectrical and Electronic EngineeringApplied Mathematics
Abstract
This paper proposes a cloud-Assisted joint charging scheduling and energy management framework for unmanned aerial vehicle (UAV) networks. For charging the UAVs those are extremely power hungry, charging towers are considered for plug-And-play charging during run-Time operations. The charging towers should be cost-effective, thus it is equipped with photovoltaic power generation and energy storage systems functionalities. Furthermore, the towers should be cooperative for more cost-effectiveness by intelligent energy sharing. Based on the needs and setting, this paper proposes 1) charging scheduling between UAVs and towers and 2) cooperative energy managements among towers. For charging scheduling, the UAVs and towers should be scheduled for maximizing charging energy amounts and the scheduled pairs should determine charging energy allocation amounts. Here, two decisions are correlated, i.e., it is a non-convex problem. We re-formulate the non-convex to convex for guaranteeing optimal solutions. Lastly, the cooperative energy sharing among towers is designed and implemented with multi-Agent deep reinforcement learning and then intelligent energy sharing can be realized. We can observe that the two methods are related and it should be managed, coordinated, and harmonized by a centralized orchestration manager under the consideration of fairness, energy-efficiency, and cost-effectiveness. Our data-intensive performance evaluation verifies that our proposed framework achieves desired performance.
ISSN
1939-9359
Language
eng
URI
https://aurora.ajou.ac.kr/handle/2018.oak/31890
https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85101820763&origin=inward
DOI
https://doi.org/10.1109/tvt.2021.3062418
Journal URL
http://ieeexplore.ieee.org/xpl/tocresult.jsp?isnumber=8039128&punumber=25
Type
Article
Funding
Manuscript received August 1, 2020; revised January 8, 2021; accepted February 24, 2021. Date of publication February 26, 2021; date of current version July 8, 2021. This work was supported by MSIT (Ministry of Science and ICT), Korea, under ITRC support Program IITP-2021-2018-0-01424 supervised by IITP (Institute for Information & Communications Technology Planning & Evaluation). The review of this article was coordinated by the Guest Editors of the Special Section on Vehicular Networks in the Era of 6G: End-Edge-Cloud Orchestrated Intelligence. (Corresponding author: Joongheon Kim; Jae-Hyun Kim.) Soyi Jung, Won Joon Yun, and Joongheon Kim are with the School of Electrical Engineering, Korea University, Seoul 02841, Republic of Korea (e-mail: sogloomy@ajou.ac.kr; ywjoon95@korea.ac.kr; joongheon@korea.ac.kr).
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Jung, Soyi Image
Jung, Soyi정소이
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