Information-based clustering and filtering for field reconstruction

Jia Chen; Akshay Malhotra; Ioannis D. Schizas

doi:10.1109/ACSSC.2015.7421195

Information-based clustering and filtering for field reconstruction

Jia Chen, Akshay Malhotra, Ioannis D. Schizas

Electrical and Computer Engineering

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

Abstract

A novel communication efficient scheme for field reconstruction is put forth. The proposed framework entails two steps. During the first step sparsity-inducing canonical correlation is utilized to determine different clusters of correlated sensors. The second step relies on least mean-squares adaptive filters to learn, at a cluster head sensor, the data model of all other cluster sensors. The cluster heads send their data and model parameters to a fusion center, which can use them to recover all sensor measurements without the need to talk to all of them. This way the communication cost can be significantly reduced. Numerical tests demonstrate the capability of the proposed scheme in field recovery.

Original language	English (US)
Title of host publication	Conference Record of the 49th Asilomar Conference on Signals, Systems and Computers, ACSSC 2015
Editors	Michael B. Matthews
Publisher	IEEE Computer Society
Pages	576-580
Number of pages	5
ISBN (Electronic)	9781467385763
DOIs	https://doi.org/10.1109/ACSSC.2015.7421195
State	Published - Feb 26 2016
Event	49th Asilomar Conference on Signals, Systems and Computers, ACSSC 2015 - Pacific Grove, United States Duration: Nov 8 2015 → Nov 11 2015

Publication series

Name	Conference Record - Asilomar Conference on Signals, Systems and Computers
Volume	2016-February
ISSN (Print)	1058-6393

Other

Other	49th Asilomar Conference on Signals, Systems and Computers, ACSSC 2015
Country/Territory	United States
City	Pacific Grove
Period	11/8/15 → 11/11/15

Keywords

Canonical correlation analysis
field reconstruction
least mean-squares adaptive filter
sparsity

Publisher link

10.1109/ACSSC.2015.7421195

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Chen, J., Malhotra, A., & Schizas, I. D. (2016). Information-based clustering and filtering for field reconstruction. In M. B. Matthews (Ed.), Conference Record of the 49th Asilomar Conference on Signals, Systems and Computers, ACSSC 2015 (pp. 576-580). [7421195] (Conference Record - Asilomar Conference on Signals, Systems and Computers; Vol. 2016-February). IEEE Computer Society. https://doi.org/10.1109/ACSSC.2015.7421195

Information-based clustering and filtering for field reconstruction. / Chen, Jia; Malhotra, Akshay; Schizas, Ioannis D.

Conference Record of the 49th Asilomar Conference on Signals, Systems and Computers, ACSSC 2015. ed. / Michael B. Matthews. IEEE Computer Society, 2016. p. 576-580 7421195 (Conference Record - Asilomar Conference on Signals, Systems and Computers; Vol. 2016-February).

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

Chen, J, Malhotra, A & Schizas, ID 2016, Information-based clustering and filtering for field reconstruction. in MB Matthews (ed.), Conference Record of the 49th Asilomar Conference on Signals, Systems and Computers, ACSSC 2015., 7421195, Conference Record - Asilomar Conference on Signals, Systems and Computers, vol. 2016-February, IEEE Computer Society, pp. 576-580, 49th Asilomar Conference on Signals, Systems and Computers, ACSSC 2015, Pacific Grove, United States, 11/8/15. https://doi.org/10.1109/ACSSC.2015.7421195

Chen J, Malhotra A, Schizas ID. Information-based clustering and filtering for field reconstruction. In Matthews MB, editor, Conference Record of the 49th Asilomar Conference on Signals, Systems and Computers, ACSSC 2015. IEEE Computer Society. 2016. p. 576-580. 7421195. (Conference Record - Asilomar Conference on Signals, Systems and Computers). doi: 10.1109/ACSSC.2015.7421195

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abstract = "A novel communication efficient scheme for field reconstruction is put forth. The proposed framework entails two steps. During the first step sparsity-inducing canonical correlation is utilized to determine different clusters of correlated sensors. The second step relies on least mean-squares adaptive filters to learn, at a cluster head sensor, the data model of all other cluster sensors. The cluster heads send their data and model parameters to a fusion center, which can use them to recover all sensor measurements without the need to talk to all of them. This way the communication cost can be significantly reduced. Numerical tests demonstrate the capability of the proposed scheme in field recovery.",

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AB - A novel communication efficient scheme for field reconstruction is put forth. The proposed framework entails two steps. During the first step sparsity-inducing canonical correlation is utilized to determine different clusters of correlated sensors. The second step relies on least mean-squares adaptive filters to learn, at a cluster head sensor, the data model of all other cluster sensors. The cluster heads send their data and model parameters to a fusion center, which can use them to recover all sensor measurements without the need to talk to all of them. This way the communication cost can be significantly reduced. Numerical tests demonstrate the capability of the proposed scheme in field recovery.

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KW - sparsity

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Information-based clustering and filtering for field reconstruction

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