Quantifying sensing quality of crowd sensing networks with confidence interval

Xiang, C; Yang, P; Fan, X; Gong, L

Quantifying sensing quality of crowd sensing networks with confidence interval

Xiang, C Yang, P Fan, X

Gong, L

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Publication Type:: Conference Proceeding
Citation:: 2017 IEEE SmartWorld Ubiquitous Intelligence and Computing, Advanced and Trusted Computed, Scalable Computing and Communications, Cloud and Big Data Computing, Internet of People and Smart City Innovation, SmartWorld/SCALCOM/UIC/ATC/CBDCom/IOP/SCI 2017 - Conference Proceedings, 2018, pp. 1 - 6
Issue Date:: 2018-06-26

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Field	Value	Language
dc.contributor.author	Xiang, C	en_US
dc.contributor.author	Yang, P	en_US
dc.contributor.author	Fan, X https://orcid.org/0000-0001-8945-3046	en_US
dc.contributor.author	Gong, L	en_US
dc.date.issued	2018-06-26	en_US
dc.identifier.citation	2017 IEEE SmartWorld Ubiquitous Intelligence and Computing, Advanced and Trusted Computed, Scalable Computing and Communications, Cloud and Big Data Computing, Internet of People and Smart City Innovation, SmartWorld/SCALCOM/UIC/ATC/CBDCom/IOP/SCI 2017 - Conference Proceedings, 2018, pp. 1 - 6	en_US
dc.identifier.isbn	9781538604342	en_US
dc.identifier.uri	http://hdl.handle.net/10453/133912
dc.description.abstract	© 2017 IEEE. Quantifying sensing quality is fundamentally important for crowd sensing networks. Existing works which focus on quantifying the sensing quality of individual user are not applicable for that of the overall crowd sensing networks. However, it is nontrivial to quantify the sensing quality of crowd sensing networks for two main challenges. First, it is difficult to quantify the sensing quality of crowd sensing networks with the uncertainty, originating from the noisy sensing data and the complex inference of sensing information. Even worse, it is nearly impossible to conduct multiple times of repeated evaluation experiments to quantify the uncertain sensing quality based on the statistical information in crowd sensing networks, due to the dynamic nature of networks and the uncontrolled mobility of users. To address these challenges, inspired by the channel capacity, we investigate the physical essence of sensing quality deeply, and propose a confidence-interval based metric to quantify the uncertain sensing quality, leveraging the lower bound of sensing variance. Moreover, we exploit the Fisher Information of the crowd sensing data to compute the confidence interval without repeating evaluation experiments for multiple times. The extensive simulations demonstrate that our method can achieve a more accurate sensing quality for crowd sensing networks than the status quo methods.	en_US
dc.relation.ispartof	2017 IEEE SmartWorld Ubiquitous Intelligence and Computing, Advanced and Trusted Computed, Scalable Computing and Communications, Cloud and Big Data Computing, Internet of People and Smart City Innovation, SmartWorld/SCALCOM/UIC/ATC/CBDCom/IOP/SCI 2017 - Conference Proceedings	en_US
dc.relation.isbasedon	10.1109/UIC-ATC.2017.8397538	en_US
dc.title	Quantifying sensing quality of crowd sensing networks with confidence interval	en_US
dc.type	Conference Proceeding
utslib.for	0805 Distributed Computing	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/Students
utslib.copyright.status	closed_access
pubs.publication-status	Published	en_US

Abstract:

© 2017 IEEE. Quantifying sensing quality is fundamentally important for crowd sensing networks. Existing works which focus on quantifying the sensing quality of individual user are not applicable for that of the overall crowd sensing networks. However, it is nontrivial to quantify the sensing quality of crowd sensing networks for two main challenges. First, it is difficult to quantify the sensing quality of crowd sensing networks with the uncertainty, originating from the noisy sensing data and the complex inference of sensing information. Even worse, it is nearly impossible to conduct multiple times of repeated evaluation experiments to quantify the uncertain sensing quality based on the statistical information in crowd sensing networks, due to the dynamic nature of networks and the uncontrolled mobility of users. To address these challenges, inspired by the channel capacity, we investigate the physical essence of sensing quality deeply, and propose a confidence-interval based metric to quantify the uncertain sensing quality, leveraging the lower bound of sensing variance. Moreover, we exploit the Fisher Information of the crowd sensing data to compute the confidence interval without repeating evaluation experiments for multiple times. The extensive simulations demonstrate that our method can achieve a more accurate sensing quality for crowd sensing networks than the status quo methods.

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http://hdl.handle.net/10453/133912