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Cooperative Multi-Agent Deep Reinforcement Learning for Reliable Surveillance via Autonomous Multi-UAV Control

Authors
Yun, W.J.Park, S.Kim, J.Shin, M.Jung, S.Mohaisen, A.Kim, J.
Issue Date
10월-2022
Publisher
IEEE Computer Society
Keywords
Electronic mail; Image resolution; Multi-agent systems; Multi-agent systems; Neural networks; Optimization; Reliability; Surveillance; Surveillance; Uncertainty; Unmanned aerial vehicle (UAV)
Citation
IEEE Transactions on Industrial Informatics, v.18, no.10, pp.7086 - 7096
Indexed
SCIE
SCOPUS
Journal Title
IEEE Transactions on Industrial Informatics
Volume
18
Number
10
Start Page
7086
End Page
7096
URI
https://scholar.korea.ac.kr/handle/2021.sw.korea/143187
DOI
10.1109/TII.2022.3143175
ISSN
1551-3203
Abstract
CCTV-based surveillance using unmanned aerial vehicles (UAVs) is considered a key technology for security in smart city environments. This paper creates a case where the UAVs with CCTV-cameras fly over the city area for flexible and reliable surveillance services. For a reliable surveillance UAV system, UAVs should be deployed to observe wide areas while minimizing overlapping and shadow areas. However, the operation of UAVs is subject to high uncertainty, necessitating autonomous recovery systems. This work develops a multi-agent deep reinforcement learning-based management scheme for reliable industry surveillance in smart city applications. The core idea this paper employs is autonomously replenishing the UAV's deficient network requirements with communications. Via intensive simulations, our proposed algorithm outperforms the state-of-the-art algorithms in terms of surveillance coverage, user support capability, and computational costs. IEEE
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Kim, Joong heon
공과대학 (전기전자공학부)
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