Crowdsourcing and Sensing for Indoor Localization in IoT: A Review

Lashkari, B; Rezazadeh, J; Farahbakhsh, R; Sandrasegaran, K

Crowdsourcing and Sensing for Indoor Localization in IoT: A Review

Lashkari, B Rezazadeh, J

Farahbakhsh, R Sandrasegaran, K

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Publication Type:: Journal Article
Citation:: IEEE Sensors Journal, 2019, 19 (7), pp. 2408 - 2434
Issue Date:: 2019-04-01

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Field	Value	Language
dc.contributor.author	Lashkari, B	en_US
dc.contributor.author	Rezazadeh, J https://orcid.org/0000-0002-6851-0117	en_US
dc.contributor.author	Farahbakhsh, R	en_US
dc.contributor.author	Sandrasegaran, K https://orcid.org/0000-0003-1270-5756	en_US
dc.date.issued	2019-04-01	en_US
dc.identifier.citation	IEEE Sensors Journal, 2019, 19 (7), pp. 2408 - 2434	en_US
dc.identifier.issn	1530-437X	en_US
dc.identifier.uri	http://hdl.handle.net/10453/131800
dc.description.abstract	© 2001-2012 IEEE. Among the emerging concepts in the context of IoT, crowdsourcing, and crowdsensing are known as two critical building blocks on the intersection point of things and human-based techniques. Although contribution and involvement of people is a key factor in both crowdsensing and crowdsourcing systems which provides reliability and data quality, level of human contribution is what makes crowd-powered systems different. To scrutinize this difference it is considered that user intervention in crowd-based schemes can be either implicit or explicit. Since one of the most important applications of crowdsourcing and crowdsensing systems is providing location services in indoor environments, in this paper, we study the level of user contribution in available crowd-powered techniques and propose a classification for crowd-powered indoor localization solutions to clarify which crowds-based approach is utilized in each indoor localization solution because in many cases the distinction is not clear and often leads to misconception. Dependency on site survey process is considered as another distinction point in our proposed classification. Hence, we consider indoor localization solutions with implicit user participation and latent mobile utilization as crowdsensing indoor localization systems. Respectively, indoor localization solutions with explicit user participation and manifest mobile utilization are classified as crowdsourcing indoor localization systems.	en_US
dc.relation.ispartof	IEEE Sensors Journal	en_US
dc.relation.isbasedon	10.1109/JSEN.2018.2880180	en_US
dc.rights	info:eu-repo/semantics/closedAccess
dc.subject.classification	Analytical Chemistry	en_US
dc.title	Crowdsourcing and Sensing for Indoor Localization in IoT: A Review	en_US
dc.type	Journal Article
utslib.citation.volume	7	en_US
utslib.citation.volume	19	en_US
utslib.for	0913 Mechanical Engineering	en_US
utslib.for	0205 Optical Physics	en_US
utslib.for	0906 Electrical and Electronic Engineering	en_US
pubs.embargo.period	Not known	en_US
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/Strength - CRIN - Realtime Information Networks
utslib.copyright.status	closed_access	*
pubs.issue	7	en_US
pubs.publication-status	Published	en_US
pubs.volume	19	en_US

Abstract:

© 2001-2012 IEEE. Among the emerging concepts in the context of IoT, crowdsourcing, and crowdsensing are known as two critical building blocks on the intersection point of things and human-based techniques. Although contribution and involvement of people is a key factor in both crowdsensing and crowdsourcing systems which provides reliability and data quality, level of human contribution is what makes crowd-powered systems different. To scrutinize this difference it is considered that user intervention in crowd-based schemes can be either implicit or explicit. Since one of the most important applications of crowdsourcing and crowdsensing systems is providing location services in indoor environments, in this paper, we study the level of user contribution in available crowd-powered techniques and propose a classification for crowd-powered indoor localization solutions to clarify which crowds-based approach is utilized in each indoor localization solution because in many cases the distinction is not clear and often leads to misconception. Dependency on site survey process is considered as another distinction point in our proposed classification. Hence, we consider indoor localization solutions with implicit user participation and latent mobile utilization as crowdsensing indoor localization systems. Respectively, indoor localization solutions with explicit user participation and manifest mobile utilization are classified as crowdsourcing indoor localization systems.

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

http://hdl.handle.net/10453/128574