CapsLoc: A Robust Indoor Localization System with WiFi Fingerprinting Using Capsule Networks

Ye, Q; Fan, X; Fang, G; Bie, H; Song, X; Shankaran, R

CapsLoc: A Robust Indoor Localization System with WiFi Fingerprinting Using Capsule Networks

Ye, Q Fan, X Fang, G Bie, H Song, X Shankaran, R

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Publisher:: IEEE
Publication Type:: Conference Proceeding
Citation:: ICC 2020 - 2020 IEEE International Conference on Communications (ICC), 2020, 2020-June, pp. 1-6
Issue Date:: 2020-07-27

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Field	Value	Language
dc.contributor.author	Ye, Q
dc.contributor.author	Fan, X
dc.contributor.author	Fang, G
dc.contributor.author	Bie, H
dc.contributor.author	Song, X
dc.contributor.author	Shankaran, R
dc.date	2020-06-07
dc.date.accessioned	2021-03-31T01:19:52Z
dc.date.available	2021-03-31T01:19:52Z
dc.date.issued	2020-07-27
dc.identifier.citation	ICC 2020 - 2020 IEEE International Conference on Communications (ICC), 2020, 2020-June, pp. 1-6
dc.identifier.isbn	978-1-7281-5090-1
dc.identifier.issn	1550-3607
dc.identifier.uri	http://hdl.handle.net/10453/147709
dc.description.abstract	With the unprecedented demand of location-based services in indoor scenarios, wireless indoor localization is emerging as an essential application for mobile users. While the line-of-sight GPS signal is not available at indoor spaces, WiFi fingerprinting using received signal strength (RSS) has become popular with its ubiquitous accessibility. Although the fingerprinting data can be easily collected by portable mobile devices, to achieve robust and efficient indoor localization remains challenging with two constraints. First, the localization accuracy will be degraded by the random fluctuation of signals that caused by multipath effects from RSS signals. Second, indoor localization algorithms are time-consuming due to the handcrafting features and complex filtering on raw dataset. To achieve high localization accuracy with WiFi fingerprinting, in this paper, we propose CapsLoc, a robust indoor localization system by using capsule networks. Specifically, the capsule network model can efficiently extract hierarchical structures from WiFi fingerprint with three main components, including a convolutional layer, a primary capsule layer and a feature capsule layer. We conduct a real-world experimental field test with over 33600 data points. The experimental results show that CapsLoc can achieve accurate indoor localization with an averaged error of 0.68 m, which outperforms conventional machine learning methods (KNN and SVM) and existing deep learning methods (CNN and SAE-CNN).
dc.language	en
dc.publisher	IEEE
dc.relation.ispartof	ICC 2020 - 2020 IEEE International Conference on Communications (ICC)
dc.relation.ispartof	ICC 2020 - 2020 IEEE International Conference on Communications
dc.relation.isbasedon	10.1109/icc40277.2020.9148933
dc.rights	© 2020 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.	en_US
dc.rights	info:eu-repo/semantics/closedAccess
dc.title	CapsLoc: A Robust Indoor Localization System with WiFi Fingerprinting Using Capsule Networks
dc.type	Conference Proceeding
utslib.citation.volume	2020-June
utslib.location.activity	Dublin, Ireland
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 - 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	*
dc.date.updated	2021-03-31T01:19:42Z
pubs.finish-date	2020-06-11
pubs.publication-status	Published
pubs.start-date	2020-06-07
pubs.volume	2020-June

Abstract:

With the unprecedented demand of location-based services in indoor scenarios, wireless indoor localization is emerging as an essential application for mobile users. While the line-of-sight GPS signal is not available at indoor spaces, WiFi fingerprinting using received signal strength (RSS) has become popular with its ubiquitous accessibility. Although the fingerprinting data can be easily collected by portable mobile devices, to achieve robust and efficient indoor localization remains challenging with two constraints. First, the localization accuracy will be degraded by the random fluctuation of signals that caused by multipath effects from RSS signals. Second, indoor localization algorithms are time-consuming due to the handcrafting features and complex filtering on raw dataset. To achieve high localization accuracy with WiFi fingerprinting, in this paper, we propose CapsLoc, a robust indoor localization system by using capsule networks. Specifically, the capsule network model can efficiently extract hierarchical structures from WiFi fingerprint with three main components, including a convolutional layer, a primary capsule layer and a feature capsule layer. We conduct a real-world experimental field test with over 33600 data points. The experimental results show that CapsLoc can achieve accurate indoor localization with an averaged error of 0.68 m, which outperforms conventional machine learning methods (KNN and SVM) and existing deep learning methods (CNN and SAE-CNN).

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http://hdl.handle.net/10453/147709