The embedded triangles algorithm for distributed estimation in sensor networks

V. Delouille; R. Neelamani; V. Chandrasekaran; R. G. Baraniuk

doi:10.1109/SSP.2003.1289422

The embedded triangles algorithm for distributed estimation in sensor networks

V. Delouille, R. Neelamani, V. Chandrasekaran, R. G. Baraniuk

Houston Methodist

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

8 Scopus citations

Abstract

We propose a new iterative distributed estimation algorithm for Gaussian hidden Markov graphical models with loops. We decompose a loopy graph into a number of linked embedded triangles and then apply a parallel block-Jacobi iteration comprising local linear minimum mean-square-error estimation on each triangle (involving a simple 3x3 matrix inverse computation) followed by an information exchange between neighboring nodes and triangles. A simulation study demonstrates that the algorithm converges extremely rapidly, outperforming a number of existing algorithms. Embedded triangles are simple, local, scalable, fault-tolerant, and energy-efficient, and thus ideally suited for wireless sensor networks.

Original language	English (US)
Title of host publication	Proceedings of the 2003 IEEE Workshop on Statistical Signal Processing, SSP 2003
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	371-374
Number of pages	4
ISBN (Electronic)	0780379977
DOIs	https://doi.org/10.1109/SSP.2003.1289422
State	Published - 2003
Event	IEEE Workshop on Statistical Signal Processing, SSP 2003 - St. Louis, United States Duration: Sep 28 2003 → Oct 1 2003

Publication series

Name	IEEE Workshop on Statistical Signal Processing Proceedings
Volume	2003-January

Other

Other	IEEE Workshop on Statistical Signal Processing, SSP 2003
Country/Territory	United States
City	St. Louis
Period	9/28/03 → 10/1/03

Keywords

Computational modeling
Concurrent computing
Embedded computing
Energy efficiency
Fault tolerance
Graphical models
Hidden Markov models
Iterative algorithms
Matrix decomposition
Wireless sensor networks

ASJC Scopus subject areas

Electrical and Electronic Engineering
Applied Mathematics
Signal Processing
Computer Science Applications

Access to Document

10.1109/SSP.2003.1289422

Cite this

Delouille, V., Neelamani, R., Chandrasekaran, V., & Baraniuk, R. G. (2003). The embedded triangles algorithm for distributed estimation in sensor networks. In Proceedings of the 2003 IEEE Workshop on Statistical Signal Processing, SSP 2003 (pp. 371-374). Article 1289422 (IEEE Workshop on Statistical Signal Processing Proceedings; Vol. 2003-January). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/SSP.2003.1289422

The embedded triangles algorithm for distributed estimation in sensor networks. / Delouille, V.; Neelamani, R.; Chandrasekaran, V. et al.
Proceedings of the 2003 IEEE Workshop on Statistical Signal Processing, SSP 2003. Institute of Electrical and Electronics Engineers Inc., 2003. p. 371-374 1289422 (IEEE Workshop on Statistical Signal Processing Proceedings; Vol. 2003-January).

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

Delouille, V, Neelamani, R, Chandrasekaran, V & Baraniuk, RG 2003, The embedded triangles algorithm for distributed estimation in sensor networks. in Proceedings of the 2003 IEEE Workshop on Statistical Signal Processing, SSP 2003., 1289422, IEEE Workshop on Statistical Signal Processing Proceedings, vol. 2003-January, Institute of Electrical and Electronics Engineers Inc., pp. 371-374, IEEE Workshop on Statistical Signal Processing, SSP 2003, St. Louis, United States, 9/28/03. https://doi.org/10.1109/SSP.2003.1289422

Delouille V, Neelamani R, Chandrasekaran V, Baraniuk RG. The embedded triangles algorithm for distributed estimation in sensor networks. In Proceedings of the 2003 IEEE Workshop on Statistical Signal Processing, SSP 2003. Institute of Electrical and Electronics Engineers Inc. 2003. p. 371-374. 1289422. (IEEE Workshop on Statistical Signal Processing Proceedings). doi: 10.1109/SSP.2003.1289422

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abstract = "We propose a new iterative distributed estimation algorithm for Gaussian hidden Markov graphical models with loops. We decompose a loopy graph into a number of linked embedded triangles and then apply a parallel block-Jacobi iteration comprising local linear minimum mean-square-error estimation on each triangle (involving a simple 3x3 matrix inverse computation) followed by an information exchange between neighboring nodes and triangles. A simulation study demonstrates that the algorithm converges extremely rapidly, outperforming a number of existing algorithms. Embedded triangles are simple, local, scalable, fault-tolerant, and energy-efficient, and thus ideally suited for wireless sensor networks.",

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N2 - We propose a new iterative distributed estimation algorithm for Gaussian hidden Markov graphical models with loops. We decompose a loopy graph into a number of linked embedded triangles and then apply a parallel block-Jacobi iteration comprising local linear minimum mean-square-error estimation on each triangle (involving a simple 3x3 matrix inverse computation) followed by an information exchange between neighboring nodes and triangles. A simulation study demonstrates that the algorithm converges extremely rapidly, outperforming a number of existing algorithms. Embedded triangles are simple, local, scalable, fault-tolerant, and energy-efficient, and thus ideally suited for wireless sensor networks.

AB - We propose a new iterative distributed estimation algorithm for Gaussian hidden Markov graphical models with loops. We decompose a loopy graph into a number of linked embedded triangles and then apply a parallel block-Jacobi iteration comprising local linear minimum mean-square-error estimation on each triangle (involving a simple 3x3 matrix inverse computation) followed by an information exchange between neighboring nodes and triangles. A simulation study demonstrates that the algorithm converges extremely rapidly, outperforming a number of existing algorithms. Embedded triangles are simple, local, scalable, fault-tolerant, and energy-efficient, and thus ideally suited for wireless sensor networks.

KW - Computational modeling

KW - Concurrent computing

KW - Embedded computing

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The embedded triangles algorithm for distributed estimation in sensor networks

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Keywords

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