Fisher information-empowered sensing quality quantification for crowdsensing networks

Xiang, C; Fan, X; Chen, C; Gong, L; Guo, S

Fisher information-empowered sensing quality quantification for crowdsensing networks

Xiang, C Fan, X

Chen, C Gong, L Guo, S

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Publisher:: Springer Science and Business Media LLC
Publication Type:: Journal Article
Citation:: Neural Computing and Applications, 2021, 33, (13), pp. 7563-7574
Issue Date:: 2021-07-01

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Field	Value	Language
dc.contributor.author	Xiang, C
dc.contributor.author	Fan, X https://orcid.org/0000-0001-8945-3046
dc.contributor.author	Chen, C
dc.contributor.author	Gong, L
dc.contributor.author	Guo, S
dc.date.accessioned	2021-10-16T22:08:48Z
dc.date.available	2021-10-16T22:08:48Z
dc.date.issued	2021-07-01
dc.identifier.citation	Neural Computing and Applications, 2021, 33, (13), pp. 7563-7574
dc.identifier.issn	0941-0643
dc.identifier.issn	1433-3058
dc.identifier.uri	http://hdl.handle.net/10453/151114
dc.description.abstract	The sensing quality is critical for crowdsensing networks. However, it is still challenging to achieve reliable and accurate quantification on sensing quality. As most existing methods only quantify individual-level sensing quality, they cannot be simply extended to measure network-level sensing quality. Meanwhile, there are two critical challenges for the non-trivial quantifications of network-level sensing quality. First, it is quite daunting to accurately measure the sensing quality, due to the uncertainties in crowdsensing, such as random noise in sensing data and complicated inference of information. Second, it is incredibly difficult to conduct repeatable experiments for multiple times, with the natural dynamics of crowdsensing networks as well as the uncontrollable behaviors of crowdsensing participants. To address the above challenges, in this work, we devise a novel quantification metric to measure the uncertain sensing quality of crowdsensing networks. The proposed metric is based on the confidence interval, exploiting the asymptotic normality property of unbiased estimations. We further leverage the Fisher information in crowdsensing data and successfully derive the confidence interval without redundant multiple computations on repeated experiments. The trace-driven evaluations demonstrate that the proposed method can achieve remarkably accurate quantifications on the network-level sensing quality, outperforming the existing works.
dc.language	en
dc.publisher	Springer Science and Business Media LLC
dc.relation.ispartof	Neural Computing and Applications
dc.relation.isbasedon	10.1007/s00521-020-05501-6
dc.rights	info:eu-repo/semantics/closedAccess
dc.subject	0801 Artificial Intelligence and Image Processing, 0906 Electrical and Electronic Engineering, 1702 Cognitive Sciences
dc.subject.classification	Artificial Intelligence & Image Processing
dc.title	Fisher information-empowered sensing quality quantification for crowdsensing networks
dc.type	Journal Article
utslib.citation.volume	33
utslib.for	0801 Artificial Intelligence and Image Processing
utslib.for	0906 Electrical and Electronic Engineering
utslib.for	1702 Cognitive Sciences
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
utslib.copyright.status	closed_access	*
dc.date.updated	2021-10-16T22:08:46Z
pubs.issue	13
pubs.publication-status	Published
pubs.volume	33
utslib.citation.issue	13

Abstract:

The sensing quality is critical for crowdsensing networks. However, it is still challenging to achieve reliable and accurate quantification on sensing quality. As most existing methods only quantify individual-level sensing quality, they cannot be simply extended to measure network-level sensing quality. Meanwhile, there are two critical challenges for the non-trivial quantifications of network-level sensing quality. First, it is quite daunting to accurately measure the sensing quality, due to the uncertainties in crowdsensing, such as random noise in sensing data and complicated inference of information. Second, it is incredibly difficult to conduct repeatable experiments for multiple times, with the natural dynamics of crowdsensing networks as well as the uncontrollable behaviors of crowdsensing participants. To address the above challenges, in this work, we devise a novel quantification metric to measure the uncertain sensing quality of crowdsensing networks. The proposed metric is based on the confidence interval, exploiting the asymptotic normality property of unbiased estimations. We further leverage the Fisher information in crowdsensing data and successfully derive the confidence interval without redundant multiple computations on repeated experiments. The trace-driven evaluations demonstrate that the proposed method can achieve remarkably accurate quantifications on the network-level sensing quality, outperforming the existing works.

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http://hdl.handle.net/10453/151114