Distributed multidimensional scaling with relative error-based neighborhood selection for node localization in sensor networks

Haiyong Luo; Jintao Li; Zhenmin Zhu; Wu Yuan; Fang Zhao; Quan Lin

doi:10.1109/ICITECHNOLOGY.2007.4290417

Distributed multidimensional scaling with relative error-based neighborhood selection for node localization in sensor networks

Haiyong Luo^*, Jintao Li, Zhenmin Zhu, Wu Yuan, Fang Zhao, Quan Lin

^*Corresponding author for this work

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

7 Citations (Scopus)

Abstract

This paper introduces a distributed weighted-multidimensional scaling algorithm that adaptively emphasizes the lowest relative error within the sensor networks. Each node adaptively chooses a neighborhood of sensors within 1 or 2 hops based on their relative errors and network density, then updates its position estimate by minimizing a local cost function and last passes this update to its neighboring sensors. For received signal-strength (RSS) based range measurements, we demonstrate performance via extensive simulation that location estimates are better than dwMDS(G), especially with rough initial global coordinates and in anisotropic topologies.

Original language	English
Title of host publication	IEEE ICIT 2007 - 2007 IEEE International Conferenceon Integration Technology
Pages	735-739
Number of pages	5
DOIs	https://doi.org/10.1109/ICITECHNOLOGY.2007.4290417
Publication status	Published - 2007
Externally published	Yes
Event	2007 IEEE International Conference on Integration Technology, ICIT 2007 - Shenzhen, China Duration: 20 Mar 2007 → 24 Mar 2007

Publication series

Name	IEEE ICIT 2007 - 2007 IEEE International Conference on Integration Technology

Conference

Conference	2007 IEEE International Conference on Integration Technology, ICIT 2007
Country/Territory	China
City	Shenzhen
Period	20/03/07 → 24/03/07

Keywords

Adaptive neighborhood selection
Distributed multidimensional scaling
Localization
Relative error
Wireless sensor networks

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10.1109/ICITECHNOLOGY.2007.4290417

Cite this

Luo, H., Li, J., Zhu, Z., Yuan, W., Zhao, F., & Lin, Q. (2007). Distributed multidimensional scaling with relative error-based neighborhood selection for node localization in sensor networks. In IEEE ICIT 2007 - 2007 IEEE International Conferenceon Integration Technology (pp. 735-739). Article 4290417 (IEEE ICIT 2007 - 2007 IEEE International Conference on Integration Technology). https://doi.org/10.1109/ICITECHNOLOGY.2007.4290417

@inproceedings{07853197c1674b3085b108e2014a6b77,

title = "Distributed multidimensional scaling with relative error-based neighborhood selection for node localization in sensor networks",

abstract = "This paper introduces a distributed weighted-multidimensional scaling algorithm that adaptively emphasizes the lowest relative error within the sensor networks. Each node adaptively chooses a neighborhood of sensors within 1 or 2 hops based on their relative errors and network density, then updates its position estimate by minimizing a local cost function and last passes this update to its neighboring sensors. For received signal-strength (RSS) based range measurements, we demonstrate performance via extensive simulation that location estimates are better than dwMDS(G), especially with rough initial global coordinates and in anisotropic topologies.",

keywords = "Adaptive neighborhood selection, Distributed multidimensional scaling, Localization, Relative error, Wireless sensor networks",

author = "Haiyong Luo and Jintao Li and Zhenmin Zhu and Wu Yuan and Fang Zhao and Quan Lin",

year = "2007",

doi = "10.1109/ICITECHNOLOGY.2007.4290417",

language = "English",

isbn = "1424410924",

series = "IEEE ICIT 2007 - 2007 IEEE International Conference on Integration Technology",

pages = "735--739",

booktitle = "IEEE ICIT 2007 - 2007 IEEE International Conferenceon Integration Technology",

note = "2007 IEEE International Conference on Integration Technology, ICIT 2007 ; Conference date: 20-03-2007 Through 24-03-2007",

}

Luo, H, Li, J, Zhu, Z, Yuan, W, Zhao, F & Lin, Q 2007, Distributed multidimensional scaling with relative error-based neighborhood selection for node localization in sensor networks. in IEEE ICIT 2007 - 2007 IEEE International Conferenceon Integration Technology., 4290417, IEEE ICIT 2007 - 2007 IEEE International Conference on Integration Technology, pp. 735-739, 2007 IEEE International Conference on Integration Technology, ICIT 2007, Shenzhen, China, 20/03/07. https://doi.org/10.1109/ICITECHNOLOGY.2007.4290417

Distributed multidimensional scaling with relative error-based neighborhood selection for node localization in sensor networks. / Luo, Haiyong; Li, Jintao; Zhu, Zhenmin et al.
IEEE ICIT 2007 - 2007 IEEE International Conferenceon Integration Technology. 2007. p. 735-739 4290417 (IEEE ICIT 2007 - 2007 IEEE International Conference on Integration Technology).

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

TY - GEN

T1 - Distributed multidimensional scaling with relative error-based neighborhood selection for node localization in sensor networks

AU - Luo, Haiyong

AU - Li, Jintao

AU - Zhu, Zhenmin

AU - Yuan, Wu

AU - Zhao, Fang

AU - Lin, Quan

PY - 2007

Y1 - 2007

N2 - This paper introduces a distributed weighted-multidimensional scaling algorithm that adaptively emphasizes the lowest relative error within the sensor networks. Each node adaptively chooses a neighborhood of sensors within 1 or 2 hops based on their relative errors and network density, then updates its position estimate by minimizing a local cost function and last passes this update to its neighboring sensors. For received signal-strength (RSS) based range measurements, we demonstrate performance via extensive simulation that location estimates are better than dwMDS(G), especially with rough initial global coordinates and in anisotropic topologies.

AB - This paper introduces a distributed weighted-multidimensional scaling algorithm that adaptively emphasizes the lowest relative error within the sensor networks. Each node adaptively chooses a neighborhood of sensors within 1 or 2 hops based on their relative errors and network density, then updates its position estimate by minimizing a local cost function and last passes this update to its neighboring sensors. For received signal-strength (RSS) based range measurements, we demonstrate performance via extensive simulation that location estimates are better than dwMDS(G), especially with rough initial global coordinates and in anisotropic topologies.

KW - Adaptive neighborhood selection

KW - Distributed multidimensional scaling

KW - Localization

KW - Relative error

KW - Wireless sensor networks

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U2 - 10.1109/ICITECHNOLOGY.2007.4290417

DO - 10.1109/ICITECHNOLOGY.2007.4290417

M3 - Conference contribution

AN - SCOPUS:42449100388

SN - 1424410924

SN - 9781424410927

T3 - IEEE ICIT 2007 - 2007 IEEE International Conference on Integration Technology

SP - 735

EP - 739

BT - IEEE ICIT 2007 - 2007 IEEE International Conferenceon Integration Technology

T2 - 2007 IEEE International Conference on Integration Technology, ICIT 2007

Y2 - 20 March 2007 through 24 March 2007

ER -

Luo H, Li J, Zhu Z, Yuan W, Zhao F, Lin Q. Distributed multidimensional scaling with relative error-based neighborhood selection for node localization in sensor networks. In IEEE ICIT 2007 - 2007 IEEE International Conferenceon Integration Technology. 2007. p. 735-739. 4290417. (IEEE ICIT 2007 - 2007 IEEE International Conference on Integration Technology). doi: 10.1109/ICITECHNOLOGY.2007.4290417

Distributed multidimensional scaling with relative error-based neighborhood selection for node localization in sensor networks

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