Cooperative Multi-Agent Deep Reinforcement Learning for Reliable Surveillance via Autonomous Multi-UAV Control
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Yun, W.J. | - |
dc.contributor.author | Park, S. | - |
dc.contributor.author | Kim, J. | - |
dc.contributor.author | Shin, M. | - |
dc.contributor.author | Jung, S. | - |
dc.contributor.author | Mohaisen, A. | - |
dc.contributor.author | Kim, J. | - |
dc.date.accessioned | 2022-08-14T19:40:53Z | - |
dc.date.available | 2022-08-14T19:40:53Z | - |
dc.date.created | 2022-08-12 | - |
dc.date.issued | 2022-10 | - |
dc.identifier.issn | 1551-3203 | - |
dc.identifier.uri | https://scholar.korea.ac.kr/handle/2021.sw.korea/143187 | - |
dc.description.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 | - |
dc.language | English | - |
dc.language.iso | en | - |
dc.publisher | IEEE Computer Society | - |
dc.title | Cooperative Multi-Agent Deep Reinforcement Learning for Reliable Surveillance via Autonomous Multi-UAV Control | - |
dc.type | Article | - |
dc.contributor.affiliatedAuthor | Kim, J. | - |
dc.identifier.doi | 10.1109/TII.2022.3143175 | - |
dc.identifier.scopusid | 2-s2.0-85123383493 | - |
dc.identifier.wosid | 000838389400061 | - |
dc.identifier.bibliographicCitation | IEEE Transactions on Industrial Informatics, v.18, no.10, pp.7086 - 7096 | - |
dc.relation.isPartOf | IEEE Transactions on Industrial Informatics | - |
dc.citation.title | IEEE Transactions on Industrial Informatics | - |
dc.citation.volume | 18 | - |
dc.citation.number | 10 | - |
dc.citation.startPage | 7086 | - |
dc.citation.endPage | 7096 | - |
dc.type.rims | ART | - |
dc.type.docType | Article | - |
dc.description.journalClass | 1 | - |
dc.description.journalRegisteredClass | scie | - |
dc.description.journalRegisteredClass | scopus | - |
dc.relation.journalResearchArea | Automation & Control Systems | - |
dc.relation.journalResearchArea | Computer Science | - |
dc.relation.journalResearchArea | Engineering | - |
dc.relation.journalWebOfScienceCategory | Automation & Control Systems | - |
dc.relation.journalWebOfScienceCategory | Computer Science, Interdisciplinary Applications | - |
dc.relation.journalWebOfScienceCategory | Engineering, Industrial | - |
dc.subject.keywordAuthor | Electronic mail | - |
dc.subject.keywordAuthor | Image resolution | - |
dc.subject.keywordAuthor | Multi-agent systems | - |
dc.subject.keywordAuthor | Multi-agent systems | - |
dc.subject.keywordAuthor | Neural networks | - |
dc.subject.keywordAuthor | Optimization | - |
dc.subject.keywordAuthor | Reliability | - |
dc.subject.keywordAuthor | Surveillance | - |
dc.subject.keywordAuthor | Surveillance | - |
dc.subject.keywordAuthor | Uncertainty | - |
dc.subject.keywordAuthor | Unmanned aerial vehicle (UAV) | - |
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