Secure and Reliable Indoor Localization Based on Multi-Task Collaborative Learning for Large-Scale Buildings

Wang, C; Luo, J; Liu, X; He, X

Secure and Reliable Indoor Localization Based on Multi-Task Collaborative Learning for Large-Scale Buildings

Wang, C Luo, J Liu, X He, X

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Publisher:: Institute of Electrical and Electronics Engineers
Publication Type:: Journal Article
Citation:: IEEE Internet of Things Journal, 2022, 9, (22), pp. 22291-22303
Issue Date:: 2022-01-01

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Field	Value	Language
dc.contributor.author	Wang, C
dc.contributor.author	Luo, J
dc.contributor.author	Liu, X
dc.contributor.author	He, X https://orcid.org/0000-0001-8962-540X
dc.date.accessioned	2023-04-05T03:16:42Z
dc.date.available	2023-04-05T03:16:42Z
dc.date.issued	2022-01-01
dc.identifier.citation	IEEE Internet of Things Journal, 2022, 9, (22), pp. 22291-22303
dc.identifier.issn	2327-4662
dc.identifier.issn	2327-4662
dc.identifier.uri	http://hdl.handle.net/10453/169157
dc.description.abstract	Accurate and reliable indoor location estimate is crucial for many Internet of Things (IoT) applications in the era of smart buildings. However, the positioning accuracy and security of the existing positioning works cannot meet the demands in the large-scale smart buildings scenarios covering multiple multi-floor buildings. Therefore, in this paper, we focus on the reliable and accurate localization under multi-building and multi-floor environments. We propose two novel designs, including a two-step reliable feature selector, and a multi-task collaborative positioning model. Firstly, we design a two-step reliable feature selector based on a access point (AP) confidence model and manifold learning, to help select the most representative and reliable fingerprint features. Secondly, we propose a multi-task cooperative positioning model, which consists of a multi-scale feature fusion module to adaptively fuse multi-scale features and a multi-task joint learning module to effectively constrain the cumulative error of multi-scale position. Finally, based on above two, we propose a reliable multi-building and multi-floor localization method (RMBMFL), which can achieve accurate and reliable location estimates with low computational complexity in a smart building complex. We did real-world experiments in a 20,000 m2 site that covers three multi-story buildings to evaluate the performance of the proposed RMBMFL. Experimental results show that RMBMFL achieves a building identification accuracy and a floor identification accuracy of 99%, and a room-level indoor localization with an average positioning error within 2m, and outperforms state-of-the-art solutions.
dc.language	en
dc.publisher	Institute of Electrical and Electronics Engineers
dc.relation.ispartof	IEEE Internet of Things Journal
dc.relation.isbasedon	10.1109/JIOT.2021.3079151
dc.rights	info:eu-repo/semantics/closedAccess
dc.subject	0805 Distributed Computing, 1005 Communications Technologies
dc.title	Secure and Reliable Indoor Localization Based on Multi-Task Collaborative Learning for Large-Scale Buildings
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/Strength - CRIN - Realtime Information Networks
pubs.organisational-group	/University of Technology Sydney/Strength - GBDTC - Global Big Data Technologies
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Electrical and Data Engineering
utslib.copyright.status	closed_access	*
pubs.consider-herdc	false
dc.date.updated	2023-04-05T03:16:41Z
pubs.issue	22
pubs.publication-status	Published
pubs.volume	9
utslib.citation.issue	22

Abstract:

Accurate and reliable indoor location estimate is crucial for many Internet of Things (IoT) applications in the era of smart buildings. However, the positioning accuracy and security of the existing positioning works cannot meet the demands in the large-scale smart buildings scenarios covering multiple multi-floor buildings. Therefore, in this paper, we focus on the reliable and accurate localization under multi-building and multi-floor environments. We propose two novel designs, including a two-step reliable feature selector, and a multi-task collaborative positioning model. Firstly, we design a two-step reliable feature selector based on a access point (AP) confidence model and manifold learning, to help select the most representative and reliable fingerprint features. Secondly, we propose a multi-task cooperative positioning model, which consists of a multi-scale feature fusion module to adaptively fuse multi-scale features and a multi-task joint learning module to effectively constrain the cumulative error of multi-scale position. Finally, based on above two, we propose a reliable multi-building and multi-floor localization method (RMBMFL), which can achieve accurate and reliable location estimates with low computational complexity in a smart building complex. We did real-world experiments in a 20,000 m2 site that covers three multi-story buildings to evaluate the performance of the proposed RMBMFL. Experimental results show that RMBMFL achieves a building identification accuracy and a floor identification accuracy of 99%, and a room-level indoor localization with an average positioning error within 2m, and outperforms state-of-the-art solutions.

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http://hdl.handle.net/10453/169157