A Novel Manifold Learning Algorithm for Localization Estimation in Wireless Sensor Networks

Shancang LI; Deyun ZHANG

doi:10.1093/ietcom/e90-b.12.3496

A Novel Manifold Learning Algorithm for Localization Estimation in Wireless Sensor Networks

Shancang LI, Deyun ZHANG

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We propose an accurate, distributed localization method that uses the connectivity measure to localize nodes in a wireless sensor network. The proposed method is based on a self-organizing isometric embedding algorithm that adaptively emphasizes the most accurate range of measurements and naturally accounts for communication constraints within the sensor network. Each node adaptively chooses a neighborhood of sensors and updates its estimate of position by minimizing a local cost function and then passes this update to the neighboring sensors. Simulations demonstrate that the proposed method is more robust to measurement error than previous methods and it can achieve comparable results using much fewer anchor nodes than previous methods.

Publication: IEICE TRANSACTIONS on Communications Vol.E90-B No.12 pp.3496-3500

Publication Date: 2007/12/01

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Online ISSN: 1745-1345

DOI: 10.1093/ietcom/e90-b.12.3496

Type of Manuscript: Special Section LETTER (Special Section on Ubiquitous Sensor Networks)

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Shancang LI, Deyun ZHANG, "A Novel Manifold Learning Algorithm for Localization Estimation in Wireless Sensor Networks" in IEICE TRANSACTIONS on Communications, vol. E90-B, no. 12, pp. 3496-3500, December 2007, doi: 10.1093/ietcom/e90-b.12.3496.
Abstract: We propose an accurate, distributed localization method that uses the connectivity measure to localize nodes in a wireless sensor network. The proposed method is based on a self-organizing isometric embedding algorithm that adaptively emphasizes the most accurate range of measurements and naturally accounts for communication constraints within the sensor network. Each node adaptively chooses a neighborhood of sensors and updates its estimate of position by minimizing a local cost function and then passes this update to the neighboring sensors. Simulations demonstrate that the proposed method is more robust to measurement error than previous methods and it can achieve comparable results using much fewer anchor nodes than previous methods.
URL: https://globals.ieice.org/en_transactions/communications/10.1093/ietcom/e90-b.12.3496/_p

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@ARTICLE{e90-b_12_3496,
author={Shancang LI, Deyun ZHANG, },
journal={IEICE TRANSACTIONS on Communications},
title={A Novel Manifold Learning Algorithm for Localization Estimation in Wireless Sensor Networks},
year={2007},
volume={E90-B},
number={12},
pages={3496-3500},
abstract={We propose an accurate, distributed localization method that uses the connectivity measure to localize nodes in a wireless sensor network. The proposed method is based on a self-organizing isometric embedding algorithm that adaptively emphasizes the most accurate range of measurements and naturally accounts for communication constraints within the sensor network. Each node adaptively chooses a neighborhood of sensors and updates its estimate of position by minimizing a local cost function and then passes this update to the neighboring sensors. Simulations demonstrate that the proposed method is more robust to measurement error than previous methods and it can achieve comparable results using much fewer anchor nodes than previous methods.},
keywords={},
doi={10.1093/ietcom/e90-b.12.3496},
ISSN={1745-1345},
month={December},}

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TY - JOUR
TI - A Novel Manifold Learning Algorithm for Localization Estimation in Wireless Sensor Networks
T2 - IEICE TRANSACTIONS on Communications
SP - 3496
EP - 3500
AU - Shancang LI
AU - Deyun ZHANG
PY - 2007
DO - 10.1093/ietcom/e90-b.12.3496
JO - IEICE TRANSACTIONS on Communications
SN - 1745-1345
VL - E90-B
IS - 12
JA - IEICE TRANSACTIONS on Communications
Y1 - December 2007
AB - We propose an accurate, distributed localization method that uses the connectivity measure to localize nodes in a wireless sensor network. The proposed method is based on a self-organizing isometric embedding algorithm that adaptively emphasizes the most accurate range of measurements and naturally accounts for communication constraints within the sensor network. Each node adaptively chooses a neighborhood of sensors and updates its estimate of position by minimizing a local cost function and then passes this update to the neighboring sensors. Simulations demonstrate that the proposed method is more robust to measurement error than previous methods and it can achieve comparable results using much fewer anchor nodes than previous methods.
ER -