Adaptive and Extensible Energy Supply Mechanism for UAVs-Aided Wireless-Powered Internet of Things

Wu, P; Xiao, F; Huang, H; Sha, C; Yu, S

Adaptive and Extensible Energy Supply Mechanism for UAVs-Aided Wireless-Powered Internet of Things

Wu, P Xiao, F Huang, H Sha, C Yu, S

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Publisher:: Institute of Electrical and Electronics Engineers
Publication Type:: Journal Article
Citation:: IEEE Internet of Things Journal, 2020, 7, (9), pp. 9201-9213
Issue Date:: 2020-09-01

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Field	Value	Language
dc.contributor.author	Wu, P
dc.contributor.author	Xiao, F
dc.contributor.author	Huang, H
dc.contributor.author	Sha, C
dc.contributor.author	Yu, S https://orcid.org/0000-0003-4485-6743
dc.date.accessioned	2020-10-27T20:01:17Z
dc.date.available	2020-10-27T20:01:17Z
dc.date.issued	2020-09-01
dc.identifier.citation	IEEE Internet of Things Journal, 2020, 7, (9), pp. 9201-9213
dc.identifier.issn	2327-4662
dc.identifier.issn	2372-2541
dc.identifier.uri	http://hdl.handle.net/10453/143545
dc.description.abstract	This article studies multiple unmanned aerial vehicles (multi-UAVs)-enabled wireless-powered Internet of Things (IoT), where a group of UAVs is dispatched as mobile power sources to charge a set of ground IoT devices. Different from the conventional radio-frequency (RF) wireless power transfer (WPT) systems, magnetic resonance-coupled (MRC) WPT systems can guarantee high power transfer efficiency without the complete alignment, which is remarkable. In this article, we extend the charging range by the wired connection between the energy receiving systems and IoT devices. Due to the restriction of carriable energy on the UAVs, designing the shortest possible trajectory for each UAV is necessary. We formulate it as a multidepots multi-UAVs trajectory optimization problem, jointly with constraints of the UAV's energy capacity and the area of the target region, to maximize the resource utilization of UAVs. To tackle this nonconvex problem, we decompose it into two subproblems, i.e., hovering locations selection and multi-UAVs trajectory optimization. For the first subproblem, we propose two approximation algorithms to obtain the near-optimal solution in the sparse networks. Then, we adopt a heuristic algorithm, a memetic algorithm-based variable neighborhood search (MAVNS), to achieve the quasioptimal trajectory rapidly. Finally, extensive numerical results are provided to evaluate the performance of the proposed algorithms. New insights are investigated on the estimation of feasibility that whether the given UAVs with energy capacity constraint can fully charge ground IoT devices within open areas.
dc.language	English
dc.publisher	Institute of Electrical and Electronics Engineers
dc.relation.ispartof	IEEE Internet of Things Journal
dc.relation.isbasedon	10.1109/JIOT.2020.3005133
dc.rights	info:eu-repo/semantics/restrictedAccess
dc.subject	0805 Distributed Computing, 1005 Communications Technologies
dc.title	Adaptive and Extensible Energy Supply Mechanism for UAVs-Aided Wireless-Powered Internet of Things
dc.type	Journal Article
utslib.citation.volume	7
utslib.for	0805 Distributed Computing
utslib.for	1005 Communications Technologies
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney
utslib.copyright.status	closed_access	*
pubs.consider-herdc	false
dc.date.updated	2020-10-27T20:01:11Z
pubs.issue	9
pubs.publication-status	Published
pubs.volume	7
utslib.citation.issue	9

Abstract:

This article studies multiple unmanned aerial vehicles (multi-UAVs)-enabled wireless-powered Internet of Things (IoT), where a group of UAVs is dispatched as mobile power sources to charge a set of ground IoT devices. Different from the conventional radio-frequency (RF) wireless power transfer (WPT) systems, magnetic resonance-coupled (MRC) WPT systems can guarantee high power transfer efficiency without the complete alignment, which is remarkable. In this article, we extend the charging range by the wired connection between the energy receiving systems and IoT devices. Due to the restriction of carriable energy on the UAVs, designing the shortest possible trajectory for each UAV is necessary. We formulate it as a multidepots multi-UAVs trajectory optimization problem, jointly with constraints of the UAV's energy capacity and the area of the target region, to maximize the resource utilization of UAVs. To tackle this nonconvex problem, we decompose it into two subproblems, i.e., hovering locations selection and multi-UAVs trajectory optimization. For the first subproblem, we propose two approximation algorithms to obtain the near-optimal solution in the sparse networks. Then, we adopt a heuristic algorithm, a memetic algorithm-based variable neighborhood search (MAVNS), to achieve the quasioptimal trajectory rapidly. Finally, extensive numerical results are provided to evaluate the performance of the proposed algorithms. New insights are investigated on the estimation of feasibility that whether the given UAVs with energy capacity constraint can fully charge ground IoT devices within open areas.

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http://hdl.handle.net/10453/143545