Use of flip ambiguity probabilities in robust sensor network localization

Kannan, AA; Fidan, B; Mao, G

Use of flip ambiguity probabilities in robust sensor network localization

Kannan, AA Fidan, B Mao, G

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Publication Type:: Journal Article
Citation:: Wireless Networks, 2011, 17 (5), pp. 1157 - 1171
Issue Date:: 2011-01-01

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Field	Value	Language
dc.contributor.author	Kannan, AA	en_US
dc.contributor.author	Fidan, B	en_US
dc.contributor.author	Mao, G https://orcid.org/0000-0002-3598-4949	en_US
dc.date.issued	2011-01-01	en_US
dc.identifier.citation	Wireless Networks, 2011, 17 (5), pp. 1157 - 1171	en_US
dc.identifier.issn	1022-0038	en_US
dc.identifier.uri	http://hdl.handle.net/10453/28283
dc.description.abstract	Collinear or near collinear placement of some sensors in a wireless sensor network causes the location estimates of nearby sensors to be sensitive to erroneous distance measurements which leads to large location estimation errors. These errors and the possible propagation of these errors to the entire network or a large portion of it, thereby causing larger estimation errors for some sensors' locations, is a major problem in localization. This phenomenon is well described in rigid graph theory, using the notion of "flip ambiguity". This paper considers arbitrary sensor neighborhoods of two dimensional sensor networks and formulates an analytical expression for the probability of occurrence of the flip ambiguity. Based on the derived probability expression, a methodology is proposed to make the localization algorithms robust by calculating such flip ambiguity probabilities and eliminating potentially poor location estimates as well as assigning confidence factors to the estimated locations to prevent them from ruining the subsequent localization steps. The efficiency of the proposed methodology is demonstrated via a set of simulations. © 2011 Springer Science+Business Media, LLC.	en_US
dc.relation.ispartof	Wireless Networks	en_US
dc.relation.isbasedon	10.1007/s11276-011-0333-z	en_US
dc.subject.classification	Networking & Telecommunications	en_US
dc.title	Use of flip ambiguity probabilities in robust sensor network localization	en_US
dc.type	Journal Article
utslib.citation.volume	5	en_US
utslib.citation.volume	17	en_US
utslib.for	0805 Distributed Computing	en_US
utslib.for	0906 Electrical and Electronic Engineering	en_US
utslib.for	1005 Communications Technologies	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	5	en_US
pubs.publication-status	Published	en_US
pubs.volume	17	en_US

Abstract:

Collinear or near collinear placement of some sensors in a wireless sensor network causes the location estimates of nearby sensors to be sensitive to erroneous distance measurements which leads to large location estimation errors. These errors and the possible propagation of these errors to the entire network or a large portion of it, thereby causing larger estimation errors for some sensors' locations, is a major problem in localization. This phenomenon is well described in rigid graph theory, using the notion of "flip ambiguity". This paper considers arbitrary sensor neighborhoods of two dimensional sensor networks and formulates an analytical expression for the probability of occurrence of the flip ambiguity. Based on the derived probability expression, a methodology is proposed to make the localization algorithms robust by calculating such flip ambiguity probabilities and eliminating potentially poor location estimates as well as assigning confidence factors to the estimated locations to prevent them from ruining the subsequent localization steps. The efficiency of the proposed methodology is demonstrated via a set of simulations. © 2011 Springer Science+Business Media, LLC.

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http://hdl.handle.net/10453/28283