Balancing Time and Energy Efficiency by Sizing Clusters: A New Data Collection Scheme in UAV-Aided Large-Scale Internet of Things

Ma, X; Huang, M; Ni, W; Yin, M; Min, J; Jamalipour, A

Balancing Time and Energy Efficiency by Sizing Clusters: A New Data Collection Scheme in UAV-Aided Large-Scale Internet of Things

Ma, X Huang, M Ni, W

Yin, M Min, J Jamalipour, A

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Publisher:: IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
Publication Type:: Journal Article
Citation:: IEEE Internet of Things Journal, 2024, 11, (6), pp. 9355-9367
Issue Date:: 2024-03-15

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Field	Value	Language
dc.contributor.author	Ma, X
dc.contributor.author	Huang, M
dc.contributor.author	Ni, W https://orcid.org/0000-0002-4933-594X
dc.contributor.author	Yin, M
dc.contributor.author	Min, J
dc.contributor.author	Jamalipour, A
dc.date.accessioned	2024-08-07T04:37:26Z
dc.date.available	2024-08-07T04:37:26Z
dc.date.issued	2024-03-15
dc.identifier.citation	IEEE Internet of Things Journal, 2024, 11, (6), pp. 9355-9367
dc.identifier.issn	2327-4662
dc.identifier.issn	2327-4662
dc.identifier.uri	http://hdl.handle.net/10453/180211
dc.description.abstract	Unmanned aerial vehicle (UAV)-aided large-scale Internet of Things (UAV-LIoT) are widely used but lack a balanced data collection (DC) scheme. To address this, we propose DC- nonorthogonal multiple access (NOMA), a new DC scheme that combines machine learning clustering with NOMA. We introduce an optimization algorithm for peak density clustering and a new LIoT clustering method. Our approach dynamically adjusts cluster size and formulates the energy-time efficiency problem as a tradeoff between energy minimization and data rate maximization. We propose a heuristic algorithm based on NOMA and an intracluster DC protocol. Experimental results show that DC- NOMA achieves balanced DC time, energy efficiency, load balance, and network lifespan extension, outperforming its benchmarks.
dc.language	English
dc.publisher	IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
dc.relation.ispartof	IEEE Internet of Things Journal
dc.relation.isbasedon	10.1109/JIOT.2023.3323321
dc.rights	info:eu-repo/semantics/closedAccess
dc.subject	0805 Distributed Computing, 1005 Communications Technologies
dc.subject.classification	40 Engineering
dc.subject.classification	46 Information and computing sciences
dc.title	Balancing Time and Energy Efficiency by Sizing Clusters: A New Data Collection Scheme in UAV-Aided Large-Scale Internet of Things
dc.type	Journal Article
utslib.citation.volume	11
utslib.for	0805 Distributed Computing
utslib.for	1005 Communications Technologies
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	2024-08-07T04:37:23Z
pubs.issue	6
pubs.publication-status	Published
pubs.volume	11
utslib.citation.issue	6

Abstract:

Unmanned aerial vehicle (UAV)-aided large-scale Internet of Things (UAV-LIoT) are widely used but lack a balanced data collection (DC) scheme. To address this, we propose DC- nonorthogonal multiple access (NOMA), a new DC scheme that combines machine learning clustering with NOMA. We introduce an optimization algorithm for peak density clustering and a new LIoT clustering method. Our approach dynamically adjusts cluster size and formulates the energy-time efficiency problem as a tradeoff between energy minimization and data rate maximization. We propose a heuristic algorithm based on NOMA and an intracluster DC protocol. Experimental results show that DC- NOMA achieves balanced DC time, energy efficiency, load balance, and network lifespan extension, outperforming its benchmarks.

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

http://hdl.handle.net/10453/180211