Presentation 2012-03-09
Path Construction for Sparsity-Constrained Network Tomography
Kazushi TAKEMOTO, Takahiro MATSUDA, Tetsuya TAKINE,
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Abstract(in English) Network tomography is an inference technique for internal network characteristics from end-to-end measurements. In this article, we consider network tomography based on compressed sensing, which is an emerging theory in signal/image processing for acquiring a sparse vector from a small number of linear measurements. When applied to network tomography, compressed sensing efficiently identifies links with lower communication quality from a small number of end-to-end measurements. We study a path construction scheme for the compressed sensing-based network tomography in networks with a limited number of measurement nodes. From sufficient conditions for recovering sparse vectors, we first derive a desirable property of measurement paths, and then propose a path construction algorithm based on the property. We implement the proposed algorithm in a network with two measurement nodes and evaluate the performance of the algorithm.
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Keyword(in English) network tomography / sparsity constraint / compressed sensing / sparse vector
Paper # IN2011-199
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Conference Date 2012/3/1(1days)
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Title (in Japanese) (See Japanese page)
Sub Title (in Japanese) (See Japanese page)
Title (in English) Path Construction for Sparsity-Constrained Network Tomography
Sub Title (in English)
Keyword(1) network tomography
Keyword(2) sparsity constraint
Keyword(3) compressed sensing
Keyword(4) sparse vector
1st Author's Name Kazushi TAKEMOTO
1st Author's Affiliation School of Engineering, Osaka University()
2nd Author's Name Takahiro MATSUDA
2nd Author's Affiliation Graduate School of Engineering, Osaka University
3rd Author's Name Tetsuya TAKINE
3rd Author's Affiliation Graduate School of Engineering, Osaka University
Date 2012-03-09
Paper # IN2011-199
Volume (vol) vol.111
Number (no) 469
Page pp.pp.-
#Pages 6
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