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

Research output: Chapter in Book/Report/Conference proceeding › Contribution to conference proceeding › Academic › peer-review

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. © 2009 Springer Berlin Heidelberg.

Original language	English
Title of host publication	Mobile Entity Localization and Tracking in GPS-less Environnments
Subtitle of host publication	Second International Workshop, MELT 2009, Orlando, FL, USA, September 30, 2009, Proceedings
Publisher	Springer Nature
Pages	163-182
Number of pages	20
ISBN (Electronic)	978-3-642-04385-7
ISBN (Print)	978-3-642-04378-9
DOIs	https://doi.org/10.1007/978-3-642-04385-7_12
Publication status	Published - 2009
Externally published	Yes
Event	International Workshop on Mobile Entity Localization and Tracking in GPS-less Environments: MELT 2009 - Orlando, United States Duration: 30 Sept 2009 → …

Workshop

Workshop	International Workshop on Mobile Entity Localization and Tracking in GPS-less Environments
Abbreviated title	MELT 2009
Country/Territory	United States
City	Orlando
Period	30/09/09 → …

Keywords

localisation
motion inference
RSSI
WLAN

Access to Document

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

Cite this

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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",

year = "2009",

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

language = "English",

isbn = "978-3-642-04378-9",

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booktitle = "Mobile Entity Localization and Tracking in GPS-less Environnments",

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address = "United Kingdom",

}

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, pp. 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. p. 163-182.

Research output: Chapter in Book/Report/Conference proceeding › Contribution to conference proceeding › Academic › peer-review

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AU - Zwaag, Berend-Jan van der

AU - Havinga, Paul J. M.

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.

PY - 2009

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N2 - 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.

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

KW - gevolgtrekking van beweging

KW - RSSI

KW - WLAN

KW - localisation

KW - motion inference

KW - RSSI

KW - WLAN

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SN - 978-3-642-04378-9

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EP - 182

BT - Mobile Entity Localization and Tracking in GPS-less Environnments

PB - Springer Nature

T2 - International Workshop on Mobile Entity Localization and Tracking in GPS-less Environments

Y2 - 30 September 2009

ER -

Inferring Motion and Location Using WLAN RSSI

Abstract

Workshop

Keywords

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