UAV Resource Cooperation Based on Reinforcement Learning

Shan, M; Xiong, J; Liu, B; Shi, Z; Li, X; Miao, N

UAV Resource Cooperation Based on Reinforcement Learning

Shan, M Xiong, J Liu, B

Shi, Z Li, X Miao, N

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Publisher:: IEEE
Publication Type:: Conference Proceeding
Citation:: 2021 IEEE International Symposium on Broadband Multimedia Systems and Broadcasting (BMSB), 2021, 2021-August
Issue Date:: 2021-08-04

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Field	Value	Language
dc.contributor.author	Shan, M
dc.contributor.author	Xiong, J
dc.contributor.author	Liu, B https://orcid.org/0000-0002-3603-6617
dc.contributor.author	Shi, Z
dc.contributor.author	Li, X
dc.contributor.author	Miao, N
dc.date	2021-08-04
dc.date.accessioned	2022-06-13T00:42:40Z
dc.date.available	2022-06-13T00:42:40Z
dc.date.issued	2021-08-04
dc.identifier.citation	2021 IEEE International Symposium on Broadband Multimedia Systems and Broadcasting (BMSB), 2021, 2021-August
dc.identifier.isbn	9781665449083
dc.identifier.issn	2155-5044
dc.identifier.issn	2155-5052
dc.identifier.uri	http://hdl.handle.net/10453/158133
dc.description.abstract	Internet of things (IoT) devices are generally incapable of transmitting data over a long distance due to their energy limitations. With the advantages of flexibility, mobility and line-of-sight links to target devices, UAV are becoming more and more widely used in data acquisition systems. Because of the limited airborne resources of UAV, we must replenish them in time. This paper focuses on the aerial replenishment strategy, which can minimizes replenishment consumption while guaranteeing the fastest completion of the mission. We employ reinforcement learning to optimize UAVs' paths and replenishment strategy. The results show that, compared to the greedy algorithm and genetic algorithm, the reinforcement learning algorithm not only has the smallest energy consumption, but also has faster convergence speed.
dc.language	en
dc.publisher	IEEE
dc.relation.ispartof	2021 IEEE International Symposium on Broadband Multimedia Systems and Broadcasting (BMSB)
dc.relation.ispartof	2021 IEEE International Symposium on Broadband Multimedia Systems and Broadcasting
dc.relation.ispartofseries	IEEE International Symposium on Broadband Multimedia Systems and Broadcasting
dc.relation.isbasedon	10.1109/bmsb53066.2021.9547180
dc.rights	info:eu-repo/semantics/closedAccess
dc.title	UAV Resource Cooperation Based on Reinforcement Learning
dc.type	Conference Proceeding
utslib.citation.volume	2021-August
utslib.location.activity	Chengdu, China
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Computer Science
utslib.copyright.status	closed_access	*
dc.date.updated	2022-06-13T00:42:39Z
pubs.finish-date	2021-08-06
pubs.publication-status	Published
pubs.start-date	2021-08-04
pubs.volume	2021-August

Abstract:

Internet of things (IoT) devices are generally incapable of transmitting data over a long distance due to their energy limitations. With the advantages of flexibility, mobility and line-of-sight links to target devices, UAV are becoming more and more widely used in data acquisition systems. Because of the limited airborne resources of UAV, we must replenish them in time. This paper focuses on the aerial replenishment strategy, which can minimizes replenishment consumption while guaranteeing the fastest completion of the mission. We employ reinforcement learning to optimize UAVs' paths and replenishment strategy. The results show that, compared to the greedy algorithm and genetic algorithm, the reinforcement learning algorithm not only has the smallest energy consumption, but also has faster convergence speed.

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http://hdl.handle.net/10453/158133