Online velocity control and data capture of drones for the internet of things: An onboard deep reinforcement learning approach

Li, K; Ni, W; Tovard, E; Jamalipour, A

Online velocity control and data capture of drones for the internet of things: An onboard deep reinforcement learning approach

Li, K Ni, W

Tovard, E Jamalipour, A

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Publisher:: IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
Publication Type:: Journal Article
Citation:: IEEE Vehicular Technology Magazine, 2021, 16, (1), pp. 49-56
Issue Date:: 2021-03-01

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	Online_Velocity_Control_and_Data_Capture_of_Drones_for_the_Internet_of_Things_An_Onboard_Deep_Reinforcement_Learning_Approach.pdf	Published version	1.54 MB	Adobe PDF	View/Open

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Field	Value	Language
dc.contributor.author	Li, K
dc.contributor.author	Ni, W https://orcid.org/0000-0002-4933-594X
dc.contributor.author	Tovard, E
dc.contributor.author	Jamalipour, A
dc.date.accessioned	2022-05-19T03:33:33Z
dc.date.available	2022-05-19T03:33:33Z
dc.date.issued	2021-03-01
dc.identifier.citation	IEEE Vehicular Technology Magazine, 2021, 16, (1), pp. 49-56
dc.identifier.issn	1556-6072
dc.identifier.issn	1556-6080
dc.identifier.uri	http://hdl.handle.net/10453/157522
dc.description.abstract	Applications of unmanned aerial vehicles (UAVs) for data collection are a promising means to extend Internet of Things (IoT) networks to remote and hostile areas and to locations where there is no access to power supplies. The adequate design of UAV velocity control and communication decision making is critical to minimize the data packet losses at ground IoT nodes that result from overflowing buffers and transmission failures. However, online velocity control and communication decision making are challenging in UAV-enabled IoT networks, due to a UAV?s lack of up-to-date knowledge about the state of the nodes, e.g., the battery energy, buffer length, and channel conditions.
dc.language	English
dc.publisher	IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
dc.relation.ispartof	IEEE Vehicular Technology Magazine
dc.relation.isbasedon	10.1109/MVT.2020.3039199
dc.rights	info:eu-repo/semantics/closedAccess
dc.subject.classification	Networking & Telecommunications
dc.title	Online velocity control and data capture of drones for the internet of things: An onboard deep reinforcement learning approach
dc.type	Journal Article
utslib.citation.volume	16
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 Electrical and Data Engineering
utslib.copyright.status	closed_access	*
dc.date.updated	2022-05-19T03:33:31Z
pubs.issue	1
pubs.publication-status	Published
pubs.volume	16
utslib.citation.issue	1

Abstract:

Applications of unmanned aerial vehicles (UAVs) for data collection are a promising means to extend Internet of Things (IoT) networks to remote and hostile areas and to locations where there is no access to power supplies. The adequate design of UAV velocity control and communication decision making is critical to minimize the data packet losses at ground IoT nodes that result from overflowing buffers and transmission failures. However, online velocity control and communication decision making are challenging in UAV-enabled IoT networks, due to a UAV?s lack of up-to-date knowledge about the state of the nodes, e.g., the battery energy, buffer length, and channel conditions.

Please use this identifier to cite or link to this item:

http://hdl.handle.net/10453/157522