Three-Dimensional Cooperative Positioning for Internet of Things Provenance

Bi, S; Cui, J; Ni, W; Jiang, Y; Yu, S; Wang, X

Three-Dimensional Cooperative Positioning for Internet of Things Provenance

Bi, S Cui, J Ni, W

Jiang, Y Yu, S

Wang, X

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Publisher:: IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
Publication Type:: Journal Article
Citation:: IEEE Internet of Things Journal, 2022, 9, (20), pp. 19945-19958
Issue Date:: 2022-10-15

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Field	Value	Language
dc.contributor.author	Bi, S
dc.contributor.author	Cui, J
dc.contributor.author	Ni, W https://orcid.org/0000-0002-4933-594X
dc.contributor.author	Jiang, Y
dc.contributor.author	Yu, S https://orcid.org/0000-0003-4485-6743
dc.contributor.author	Wang, X
dc.date.accessioned	2023-04-10T23:55:03Z
dc.date.available	2023-04-10T23:55:03Z
dc.date.issued	2022-10-15
dc.identifier.citation	IEEE Internet of Things Journal, 2022, 9, (20), pp. 19945-19958
dc.identifier.issn	2327-4662
dc.identifier.issn	2327-4662
dc.identifier.uri	http://hdl.handle.net/10453/169454
dc.description.abstract	A large number of Internet of Things (IoT) devices have been interconnected for information collection and exchange. The data are only meaningful if it is captured at the expected location (i.e., the IoT devices or sensors are not removed accidentally or intentionally). This article presents a new algorithm, which cooperatively locates multiple IoT devices deployed in a 3-D space based on pairwise Euclidean distance measurements. When the distance measurement noises are negligible, a new feasibility problem of rank-3 variables is formulated. We solve the problem using the difference-of-convex (DC) programming to preserve the rank-3 constraints, rather than relaxing the constraints, using semidefinite relaxation (SDR). When the distance measurements are corrupted by additive noises and nonlight-of-sight (NLOS) propagation, a maximum-likelihood estimation (MLE) problem is formulated and transformed to a DC program solved with the rank-3 constraints preserved. Simulation results indicate that the proposed approach can achieve satisfactory accuracy results with a low complexity and strong robustness to the irregular topology, poor connectivity, and measurement errors, as compared to existing SDR-based alternatives.
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.2022.3170564
dc.rights	info:eu-repo/semantics/closedAccess
dc.subject	0805 Distributed Computing, 1005 Communications Technologies
dc.title	Three-Dimensional Cooperative Positioning for Internet of Things Provenance
dc.type	Journal Article
utslib.citation.volume	9
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
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	2023-04-10T23:55:02Z
pubs.issue	20
pubs.publication-status	Published
pubs.volume	9
utslib.citation.issue	20

Abstract:

A large number of Internet of Things (IoT) devices have been interconnected for information collection and exchange. The data are only meaningful if it is captured at the expected location (i.e., the IoT devices or sensors are not removed accidentally or intentionally). This article presents a new algorithm, which cooperatively locates multiple IoT devices deployed in a 3-D space based on pairwise Euclidean distance measurements. When the distance measurement noises are negligible, a new feasibility problem of rank-3 variables is formulated. We solve the problem using the difference-of-convex (DC) programming to preserve the rank-3 constraints, rather than relaxing the constraints, using semidefinite relaxation (SDR). When the distance measurements are corrupted by additive noises and nonlight-of-sight (NLOS) propagation, a maximum-likelihood estimation (MLE) problem is formulated and transformed to a DC program solved with the rank-3 constraints preserved. Simulation results indicate that the proposed approach can achieve satisfactory accuracy results with a low complexity and strong robustness to the irregular topology, poor connectivity, and measurement errors, as compared to existing SDR-based alternatives.

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http://hdl.handle.net/10453/169454