Towards System Implementation and Data Analysis for Crowdsensing Based Outdoor RSS Maps

Fan, X; He, X; Xiang, C; Puthal, D; Gong, L; Nanda, P; Fang, G

Towards System Implementation and Data Analysis for Crowdsensing Based Outdoor RSS Maps

Fan, X

He, X

Xiang, C Puthal, D

Gong, L Nanda, P

Fang, G

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Publication Type:: Journal Article
Citation:: IEEE Access, 2018, 6 pp. 47535 - 47545
Issue Date:: 2018-08-28

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Field	Value	Language
dc.contributor.author	Fan, X https://orcid.org/0000-0001-8945-3046	en_US
dc.contributor.author	He, X https://orcid.org/0000-0001-8962-540X	en_US
dc.contributor.author	Xiang, C	en_US
dc.contributor.author	Puthal, D https://orcid.org/0000-0002-8332-278X	en_US
dc.contributor.author	Gong, L	en_US
dc.contributor.author	Nanda, P https://orcid.org/0000-0002-5748-155X	en_US
dc.contributor.author	Fang, G	en_US
dc.date.issued	2018-08-28	en_US
dc.identifier.citation	IEEE Access, 2018, 6 pp. 47535 - 47545	en_US
dc.identifier.uri	http://hdl.handle.net/10453/127613
dc.description.abstract	© 2013 IEEE. With the explosive usage of smart mobile devices, sustainable access to wireless networks (e.g., Wi-Fi) has become a pervasive demand. Most mobile users expect seamless network connection with low cost. Indeed, this can be achieved by using an accurate received signal strength (RSS) map of wireless access points. While existing methods are either costly or unscalable, the recently emerged mobile crowdsensing (MCS) paradigm is a promising technique for building RSS maps. MCS applications leverage pervasive mobile devices to collaboratively collect data. However, the heterogeneity of devices and the mobility of users could cause inherent noises and blank spots in collected data set. In this paper, we study how to: 1) tame the sensing noises from heterogenous mobile devices and 2) construct accurate and complete RSS maps with random mobility of crowdsensing participants. First, we build a mobile crowdsensing system called i Map to collect RSS measurements with heterogeneous mobile devices. Second, through observing experimental results, we build statistical models of sensing noises and derive different parameters for each kind of mobile device. Third, we present the signal transmission model with measurement error model, and we propose a novel signal recovery scheme to construct accurate and complete RSS maps. The evaluation results show that the proposed method can achieve 90% and 95% recovery rate in geographic coordinate system and polar coordinate system, respectively.	en_US
dc.relation.ispartof	IEEE Access	en_US
dc.relation.isbasedon	10.1109/ACCESS.2018.2867578	en_US
dc.rights	info:eu-repo/semantics/openAccess
dc.title	Towards System Implementation and Data Analysis for Crowdsensing Based Outdoor RSS Maps	en_US
dc.type	Journal Article
utslib.citation.volume	6	en_US
utslib.for	0906 Electrical and Electronic Engineering	en_US
utslib.for	08 Information and Computing Sciences	en_US
utslib.for	09 Engineering	en_US
utslib.for	10 Technology	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/Faculty of Engineering and Information Technology/School of Information, Systems and Modelling
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/Strength - INEXT - Innovation in IT Services and Applications
pubs.organisational-group	/University of Technology Sydney/Students
utslib.copyright.status	open_access	*
pubs.publication-status	Published	en_US
pubs.volume	6	en_US

Abstract:

© 2013 IEEE. With the explosive usage of smart mobile devices, sustainable access to wireless networks (e.g., Wi-Fi) has become a pervasive demand. Most mobile users expect seamless network connection with low cost. Indeed, this can be achieved by using an accurate received signal strength (RSS) map of wireless access points. While existing methods are either costly or unscalable, the recently emerged mobile crowdsensing (MCS) paradigm is a promising technique for building RSS maps. MCS applications leverage pervasive mobile devices to collaboratively collect data. However, the heterogeneity of devices and the mobility of users could cause inherent noises and blank spots in collected data set. In this paper, we study how to: 1) tame the sensing noises from heterogenous mobile devices and 2) construct accurate and complete RSS maps with random mobility of crowdsensing participants. First, we build a mobile crowdsensing system called i Map to collect RSS measurements with heterogeneous mobile devices. Second, through observing experimental results, we build statistical models of sensing noises and derive different parameters for each kind of mobile device. Third, we present the signal transmission model with measurement error model, and we propose a novel signal recovery scheme to construct accurate and complete RSS maps. The evaluation results show that the proposed method can achieve 90% and 95% recovery rate in geographic coordinate system and polar coordinate system, respectively.

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

http://hdl.handle.net/10453/127613