IEICE TRANSACTIONS on Communications
Sequential Loss Tomography Using Compressed Sensing
Summary :
Network tomography is a technique for estimating internal network characteristics from end-to-end measurements. In this paper, we focus on loss tomography, which is a network tomography problem for estimating link loss rates. We study a loss tomography problem to detect links with high link loss rates in network environments with dynamically changing link loss rates, and propose a window-based sequential loss tomography scheme. The loss tomography problem is formulated as an underdetermined linear inverse problem, where there are infinitely many candidates of the solution. In the proposed scheme, we use compressed sensing, which can solve the problem with a prior information that the solution is a sparse vector. Measurement nodes transmit probe packets on measurement paths established between them, and calculate packet loss rates of measurement paths (path loss rates) from probe packets received within a window. Measurement paths are classified into normal quality and low quality states according to the path loss rates. When a measurement node finds measurement paths in the low quality states, link loss rates are estimated by compressed sensing. Using simulation scenarios with a few link states changing dynamically from low to high link loss rates, we evaluate the performance of the proposed scheme.
- Publication
- IEICE TRANSACTIONS on Communications Vol.E96-B No.11 pp.2756-2765
- Publication Date
- 2013/11/01
- Publicized
- Online ISSN
- 1745-1345
- DOI
- 10.1587/transcom.E96.B.2756
- Type of Manuscript
- Special Section PAPER (Special Section on Progress in Information Network Science)
- Category
Authors
Kazushi TAKEMOTO
Osaka University
Takahiro MATSUDA
Osaka University
Tetsuya TAKINE
Osaka University
Keyword
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Kazushi TAKEMOTO, Takahiro MATSUDA, Tetsuya TAKINE, "Sequential Loss Tomography Using Compressed Sensing" in IEICE TRANSACTIONS on Communications,
vol. E96-B, no. 11, pp. 2756-2765, November 2013, doi: 10.1587/transcom.E96.B.2756.
Abstract: Network tomography is a technique for estimating internal network characteristics from end-to-end measurements. In this paper, we focus on loss tomography, which is a network tomography problem for estimating link loss rates. We study a loss tomography problem to detect links with high link loss rates in network environments with dynamically changing link loss rates, and propose a window-based sequential loss tomography scheme. The loss tomography problem is formulated as an underdetermined linear inverse problem, where there are infinitely many candidates of the solution. In the proposed scheme, we use compressed sensing, which can solve the problem with a prior information that the solution is a sparse vector. Measurement nodes transmit probe packets on measurement paths established between them, and calculate packet loss rates of measurement paths (path loss rates) from probe packets received within a window. Measurement paths are classified into normal quality and low quality states according to the path loss rates. When a measurement node finds measurement paths in the low quality states, link loss rates are estimated by compressed sensing. Using simulation scenarios with a few link states changing dynamically from low to high link loss rates, we evaluate the performance of the proposed scheme.
URL: https://global.ieice.org/en_transactions/communications/10.1587/transcom.E96.B.2756/_p
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@ARTICLE{e96-b_11_2756,
author={Kazushi TAKEMOTO, Takahiro MATSUDA, Tetsuya TAKINE, },
journal={IEICE TRANSACTIONS on Communications},
title={Sequential Loss Tomography Using Compressed Sensing},
year={2013},
volume={E96-B},
number={11},
pages={2756-2765},
abstract={Network tomography is a technique for estimating internal network characteristics from end-to-end measurements. In this paper, we focus on loss tomography, which is a network tomography problem for estimating link loss rates. We study a loss tomography problem to detect links with high link loss rates in network environments with dynamically changing link loss rates, and propose a window-based sequential loss tomography scheme. The loss tomography problem is formulated as an underdetermined linear inverse problem, where there are infinitely many candidates of the solution. In the proposed scheme, we use compressed sensing, which can solve the problem with a prior information that the solution is a sparse vector. Measurement nodes transmit probe packets on measurement paths established between them, and calculate packet loss rates of measurement paths (path loss rates) from probe packets received within a window. Measurement paths are classified into normal quality and low quality states according to the path loss rates. When a measurement node finds measurement paths in the low quality states, link loss rates are estimated by compressed sensing. Using simulation scenarios with a few link states changing dynamically from low to high link loss rates, we evaluate the performance of the proposed scheme.},
keywords={},
doi={10.1587/transcom.E96.B.2756},
ISSN={1745-1345},
month={November},}
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TY - JOUR
TI - Sequential Loss Tomography Using Compressed Sensing
T2 - IEICE TRANSACTIONS on Communications
SP - 2756
EP - 2765
AU - Kazushi TAKEMOTO
AU - Takahiro MATSUDA
AU - Tetsuya TAKINE
PY - 2013
DO - 10.1587/transcom.E96.B.2756
JO - IEICE TRANSACTIONS on Communications
SN - 1745-1345
VL - E96-B
IS - 11
JA - IEICE TRANSACTIONS on Communications
Y1 - November 2013
AB - Network tomography is a technique for estimating internal network characteristics from end-to-end measurements. In this paper, we focus on loss tomography, which is a network tomography problem for estimating link loss rates. We study a loss tomography problem to detect links with high link loss rates in network environments with dynamically changing link loss rates, and propose a window-based sequential loss tomography scheme. The loss tomography problem is formulated as an underdetermined linear inverse problem, where there are infinitely many candidates of the solution. In the proposed scheme, we use compressed sensing, which can solve the problem with a prior information that the solution is a sparse vector. Measurement nodes transmit probe packets on measurement paths established between them, and calculate packet loss rates of measurement paths (path loss rates) from probe packets received within a window. Measurement paths are classified into normal quality and low quality states according to the path loss rates. When a measurement node finds measurement paths in the low quality states, link loss rates are estimated by compressed sensing. Using simulation scenarios with a few link states changing dynamically from low to high link loss rates, we evaluate the performance of the proposed scheme.
ER -
English
