Inferring Motion and Location Using WLAN RSSI

Kavitha Muthukrishnan; Berend-Jan van der Zwaag; Paul J. M. Havinga

doi:10.1007/978-3-642-04385-7_12

Inferring Motion and Location Using WLAN RSSI

Kavitha Muthukrishnan, Berend-Jan van der Zwaag, Paul J. M. Havinga

University of Twente, Department of Computer Science, Pervasive Systems Group

Onderzoeksoutput: Contribution to conference proceeding › Academic › peer review

Samenvatting

We present novel algorithms to infer movement by making use of inherent fluctuations in the received signal strengths from existing WLAN infrastructure. We evaluate the performance of the presented algorithms based on classification metrics such as recall and precision using annotated traces obtained over twelve hours effectively from different types of environment and with different access point densities. We show how common deterministic localisation algorithms such as centroid and weighted centroid can improve when a motion model is included. To our knowledge, motion models are normally used only in probabilistic algorithms and such simple deterministic algorithms have not used a motion model in a principled manner. We evaluate the performance of these algorithms also against traces of RSSI data, with and without adding inferred mobility information. © 2009 Springer Berlin Heidelberg.

Originele taal-2	English
Titel	Mobile Entity Localization and Tracking in GPS-less Environnments
Subtitel	Second International Workshop, MELT 2009, Orlando, FL, USA, September 30, 2009, Proceedings
Uitgeverij	Springer Nature
Pagina's	163-182
Aantal pagina's	20
ISBN van elektronische versie	978-3-642-04385-7
ISBN van geprinte versie	978-3-642-04378-9
DOI's	https://doi.org/10.1007/978-3-642-04385-7_12
Status	Published - 2009
Extern gepubliceerd	Ja
Evenement	International Workshop on Mobile Entity Localization and Tracking in GPS-less Environments: MELT 2009 - Orlando, United States Duur: 30 sep. 2009 → …

Workshop

Workshop	International Workshop on Mobile Entity Localization and Tracking in GPS-less Environments
Verkorte titel	MELT 2009
Land/Regio	United States
Stad	Orlando
Periode	30/09/09 → …

Keywords

lokalisatie
gevolgtrekking van beweging
RSSI
WLAN

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10.1007/978-3-642-04385-7_12

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title = "Inferring Motion and Location Using WLAN RSSI",

abstract = "We present novel algorithms to infer movement by making use of inherent fluctuations in the received signal strengths from existing WLAN infrastructure. We evaluate the performance of the presented algorithms based on classification metrics such as recall and precision using annotated traces obtained over twelve hours effectively from different types of environment and with different access point densities. We show how common deterministic localisation algorithms such as centroid and weighted centroid can improve when a motion model is included. To our knowledge, motion models are normally used only in probabilistic algorithms and such simple deterministic algorithms have not used a motion model in a principled manner. We evaluate the performance of these algorithms also against traces of RSSI data, with and without adding inferred mobility information. {\textcopyright} 2009 Springer Berlin Heidelberg.",

keywords = "lokalisatie, gevolgtrekking van beweging, RSSI, WLAN, localisation, motion inference, RSSI, WLAN",

author = "Kavitha Muthukrishnan and Zwaag, {Berend-Jan van der} and Havinga, {Paul J. M.}",

note = "DBLP License: DBLP's bibliographic metadata records provided through http://dblp.org/ are distributed under a Creative Commons CC0 1.0 Universal Public Domain Dedication. Although the bibliographic metadata records are provided consistent with CC0 1.0 Dedication, the content described by the metadata records is not. Content may be subject to copyright, rights of privacy, rights of publicity and other restrictions.; International Workshop on Mobile Entity Localization and Tracking in GPS-less Environments : MELT 2009, MELT 2009 ; Conference date: 30-09-2009",

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doi = "10.1007/978-3-642-04385-7_12",

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Muthukrishnan, K, Zwaag, B-JVD & Havinga, PJM 2009, Inferring Motion and Location Using WLAN RSSI. in Mobile Entity Localization and Tracking in GPS-less Environnments: Second International Workshop, MELT 2009, Orlando, FL, USA, September 30, 2009, Proceedings. Springer Nature, blz. 163-182, International Workshop on Mobile Entity Localization and Tracking in GPS-less Environments, Orlando, United States, 30/09/09. https://doi.org/10.1007/978-3-642-04385-7_12

Inferring Motion and Location Using WLAN RSSI. / Muthukrishnan, Kavitha; Zwaag, Berend-Jan van der; Havinga, Paul J. M.
Mobile Entity Localization and Tracking in GPS-less Environnments: Second International Workshop, MELT 2009, Orlando, FL, USA, September 30, 2009, Proceedings. Springer Nature, 2009. blz. 163-182.

Onderzoeksoutput: Contribution to conference proceeding › Academic › peer review

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N1 - DBLP License: DBLP's bibliographic metadata records provided through http://dblp.org/ are distributed under a Creative Commons CC0 1.0 Universal Public Domain Dedication. Although the bibliographic metadata records are provided consistent with CC0 1.0 Dedication, the content described by the metadata records is not. Content may be subject to copyright, rights of privacy, rights of publicity and other restrictions.

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AB - We present novel algorithms to infer movement by making use of inherent fluctuations in the received signal strengths from existing WLAN infrastructure. We evaluate the performance of the presented algorithms based on classification metrics such as recall and precision using annotated traces obtained over twelve hours effectively from different types of environment and with different access point densities. We show how common deterministic localisation algorithms such as centroid and weighted centroid can improve when a motion model is included. To our knowledge, motion models are normally used only in probabilistic algorithms and such simple deterministic algorithms have not used a motion model in a principled manner. We evaluate the performance of these algorithms also against traces of RSSI data, with and without adding inferred mobility information. © 2009 Springer Berlin Heidelberg.

KW - lokalisatie

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KW - RSSI

KW - WLAN

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Inferring Motion and Location Using WLAN RSSI

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Workshop

Keywords

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