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Infrastructure-Assisted Cooperative Multi-UAV Deep Reinforcement Energy Trading Learning for Big-Data Processing
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Publication Year
2021-01-13
Journal
International Conference on Information Networking
Publisher
IEEE Computer Society
Citation
International Conference on Information Networking, Vol.2021-January, pp.159-162
Keyword
Big-data processingDeep learningMulti-agent deep reinforcement learningSmart gridUnmanned aerial vehicle (UAV).
Mesh Keyword
Aerial surveillanceCellular servicesCharging infrastructuresCharging mechanismConsistent operationData intensiveEnergy storage systemsUtility companies
All Science Classification Codes (ASJC)
Computer Networks and CommunicationsInformation Systems
Abstract
This paper proposes a cooperative multi-agent deep reinforcement learning (MADRL) algorithm for energy trading among multiple unmanned aerial vehicles (UAVs) in order to perform big-data processing in a distributed manner. In order to realize UAV-based aerial surveillance or mobile cellular services, seamless and robust wireless charging mechanisms are required for delivering energy sources from charging infrastructure (i.e., charging towers) to UAVs for the consistent operations of the UAVs in the sky. For actively and intelligently managing the charging towers, MADRL-based energy management system (EMS) is proposed and designed for energy trading among the energy storage systems those are equipped with charging towers. If the required energy for charging UAVs is not enough, the purchasing energy from utility company is desired which takes high consts. The main purpose of MADRL-based EMS learning is for minimizing purchasing energy from outside utility company for minimizing operational costs. Our data-intensive performance evaluation verifies that our proposed framework achieves desired performance.
ISSN
1976-7684
Language
eng
URI
https://aurora.ajou.ac.kr/handle/2018.oak/36688
https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85100828847&origin=inward
DOI
https://doi.org/10.1109/icoin50884.2021.9333895
Journal URL
http://www.icoin.org/
Type
Conference
Funding
This research was supported by the MSIT (Ministry of Science and ICT), Korea, under the ITRC (Information Technology Research Center) support program (IITP-2020-2017-0-01637) supervised by the IITP (Institute for Information & Communications Technology Planning & Evaluation) and also by National Research Foundation of Korea (2019R1A2C4070663, 2019M3E4A1080391). J. Kim and J.-H. Kim are the corresponding authors of this paper.
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Jung, Soyi Image
Jung, Soyi정소이
Department of Electrical and Computer Engineering
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