Nonmetric MDS for sensor localization

Vo Dinh Minh Nhat; Due Vo; Subhash Challa; Sungyoung Lee

doi:10.1109/ISWPC.2008.4556237

Nonmetric MDS for sensor localization

Vo Dinh Minh Nhat
, Due Vo
, Subhash Challa
, Sungyoung Lee

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

22 Citations (Scopus)

Abstract

Multidimensional Scaling (MDS) has been recently applied to node localization in sensor networks and gained some very impressive performance. MDS treats dissimilarities of pair-wise nodes directly as Euclidean distances and then makes use of the spectral decomposition of a doubly centered matrix of dissimilarities. However dissimilarities mainly estimated by Received Signal Strength (RSS) or by the Time of Arrival (TOA) of communication signal from the sender to the receiver used to suffer errors. From this observation, Nonmetric Multidimensional Scaling (NMDS) based only the rank order of the dissimilarities is proposed in this paper. Different from MDS, NMDS obtain insights into the nature of "perceived" dissimilarities which makes it more suitable to the problem of sensor localization. The experiment on real sensor network measurements of RSS and TOA shows the efficiency and novelty of NMDS for sensor localization problem in term of sensor location-estimated error.

Original language	English
Title of host publication	3rd International Symposium on Wireless Pervasive Computing, ISWPC 2008, Proceedings
Pages	396-400
Number of pages	5
DOIs	https://doi.org/10.1109/ISWPC.2008.4556237
Publication status	Published - 2008
Event	3rd International Symposium on Wireless Pervasive Computing, ISWPC 2008 - Santorini, Greece Duration: 7 May 2008 → 9 May 2008

Publication series

Name	3rd International Symposium on Wireless Pervasive Computing, ISWPC 2008, Proceedings

Conference

Conference	3rd International Symposium on Wireless Pervasive Computing, ISWPC 2008
Country/Territory	Greece
City	Santorini
Period	7/05/08 → 9/05/08

Bibliographical note

Copyright:
Copyright 2011 Elsevier B.V., All rights reserved.

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10.1109/ISWPC.2008.4556237

Cite this

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title = "Nonmetric MDS for sensor localization",

abstract = "Multidimensional Scaling (MDS) has been recently applied to node localization in sensor networks and gained some very impressive performance. MDS treats dissimilarities of pair-wise nodes directly as Euclidean distances and then makes use of the spectral decomposition of a doubly centered matrix of dissimilarities. However dissimilarities mainly estimated by Received Signal Strength (RSS) or by the Time of Arrival (TOA) of communication signal from the sender to the receiver used to suffer errors. From this observation, Nonmetric Multidimensional Scaling (NMDS) based only the rank order of the dissimilarities is proposed in this paper. Different from MDS, NMDS obtain insights into the nature of {"}perceived{"} dissimilarities which makes it more suitable to the problem of sensor localization. The experiment on real sensor network measurements of RSS and TOA shows the efficiency and novelty of NMDS for sensor localization problem in term of sensor location-estimated error.",

author = "Nhat, \{Vo Dinh Minh\} and Due Vo and Subhash Challa and Sungyoung Lee",

year = "2008",

doi = "10.1109/ISWPC.2008.4556237",

language = "English",

isbn = "9781424416530",

series = "3rd International Symposium on Wireless Pervasive Computing, ISWPC 2008, Proceedings",

pages = "396--400",

booktitle = "3rd International Symposium on Wireless Pervasive Computing, ISWPC 2008, Proceedings",

}

Nhat, VDM, Vo, D, Challa, S & Lee, S 2008, Nonmetric MDS for sensor localization. in 3rd International Symposium on Wireless Pervasive Computing, ISWPC 2008, Proceedings., 4556237, 3rd International Symposium on Wireless Pervasive Computing, ISWPC 2008, Proceedings, pp. 396-400, 3rd International Symposium on Wireless Pervasive Computing, ISWPC 2008, Santorini, Greece, 7/05/08. https://doi.org/10.1109/ISWPC.2008.4556237

Nonmetric MDS for sensor localization. / Nhat, Vo Dinh Minh; Vo, Due; Challa, Subhash et al.
3rd International Symposium on Wireless Pervasive Computing, ISWPC 2008, Proceedings. 2008. p. 396-400 4556237 (3rd International Symposium on Wireless Pervasive Computing, ISWPC 2008, Proceedings).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Nonmetric MDS for sensor localization

AU - Nhat, Vo Dinh Minh

AU - Vo, Due

AU - Challa, Subhash

AU - Lee, Sungyoung

PY - 2008

Y1 - 2008

N2 - Multidimensional Scaling (MDS) has been recently applied to node localization in sensor networks and gained some very impressive performance. MDS treats dissimilarities of pair-wise nodes directly as Euclidean distances and then makes use of the spectral decomposition of a doubly centered matrix of dissimilarities. However dissimilarities mainly estimated by Received Signal Strength (RSS) or by the Time of Arrival (TOA) of communication signal from the sender to the receiver used to suffer errors. From this observation, Nonmetric Multidimensional Scaling (NMDS) based only the rank order of the dissimilarities is proposed in this paper. Different from MDS, NMDS obtain insights into the nature of "perceived" dissimilarities which makes it more suitable to the problem of sensor localization. The experiment on real sensor network measurements of RSS and TOA shows the efficiency and novelty of NMDS for sensor localization problem in term of sensor location-estimated error.

AB - Multidimensional Scaling (MDS) has been recently applied to node localization in sensor networks and gained some very impressive performance. MDS treats dissimilarities of pair-wise nodes directly as Euclidean distances and then makes use of the spectral decomposition of a doubly centered matrix of dissimilarities. However dissimilarities mainly estimated by Received Signal Strength (RSS) or by the Time of Arrival (TOA) of communication signal from the sender to the receiver used to suffer errors. From this observation, Nonmetric Multidimensional Scaling (NMDS) based only the rank order of the dissimilarities is proposed in this paper. Different from MDS, NMDS obtain insights into the nature of "perceived" dissimilarities which makes it more suitable to the problem of sensor localization. The experiment on real sensor network measurements of RSS and TOA shows the efficiency and novelty of NMDS for sensor localization problem in term of sensor location-estimated error.

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M3 - Conference contribution

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BT - 3rd International Symposium on Wireless Pervasive Computing, ISWPC 2008, Proceedings

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ER -

Nonmetric MDS for sensor localization

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