Nonparametric change detection and estimation in large-scale sensor networks

Ting He; Shai Ben-David; Lang Tong

doi:10.1109/TSP.2006.870635

Nonparametric change detection and estimation in large-scale sensor networks

Ting He
, Shai Ben-David
, Lang Tong

Computer Science and Engineering

Research output: Contribution to journal › Article › peer-review

13 Scopus citations

Abstract

The problem of detecting changes in the distribution of alarmed sensors is considered. Under a nonparametric change detection framework, several detection and estimation algorithms are presented based on the Vapnik-Chervonenkis (VC) theory. Theoretical performance guarantees are obtained by providing error exponents for false-alarm and miss detection probabilities. Recursive algorithms for the efficient computation of test statistics are derived. The estimation problem is also considered in which, after detection is made, the location with maximum distribution change is estimated.

Original language	English (US)
Pages (from-to)	1204-1217
Number of pages	14
Journal	IEEE Transactions on Signal Processing
Volume	54
Issue number	4
DOIs	https://doi.org/10.1109/TSP.2006.870635
State	Published - Apr 2006

All Science Journal Classification (ASJC) codes

Signal Processing
Electrical and Electronic Engineering

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10.1109/TSP.2006.870635

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@article{1e1a078a48a64969989a3c95f40c0676,

title = "Nonparametric change detection and estimation in large-scale sensor networks",

abstract = "The problem of detecting changes in the distribution of alarmed sensors is considered. Under a nonparametric change detection framework, several detection and estimation algorithms are presented based on the Vapnik-Chervonenkis (VC) theory. Theoretical performance guarantees are obtained by providing error exponents for false-alarm and miss detection probabilities. Recursive algorithms for the efficient computation of test statistics are derived. The estimation problem is also considered in which, after detection is made, the location with maximum distribution change is estimated.",

author = "Ting He and Shai Ben-David and Lang Tong",

note = "Funding Information: Manuscript received December 9, 2004; revised June 14, 2005. This work was supported in part by the Multidisciplinary University Research Initiative (MURI) under the Office of Naval Research Contract N00014-00-1-0564, and Army Research Laboratory CTA on Communication and Networks under Grant DAAD19-01-2-0011, and the National Science Foundation under Contract CCR-0311055. Part of this work was presented at the 38th Annual Conference on Information Sciences and Systems (CISS{\textquoteright}04), Princeton, NJ, March 17–19, 2004; the 24th Army Science Conference, Orlando, FL, November 29– December 2, 2004; the 2005 IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP{\textquoteright}05), Philadelphia, PA, March 18–23, 2005; and the 2005 IEEE/AFCEA Military Communications Conference (MILCOM{\textquoteright}05), Atlantic City, NJ, October 17–20, 2005. The associate editor coordinating the review of this manuscript and approving it for publication was Dr. Chong-Yung Chi.",

year = "2006",

month = apr,

doi = "10.1109/TSP.2006.870635",

language = "English (US)",

volume = "54",

pages = "1204--1217",

journal = "IEEE Transactions on Signal Processing",

issn = "1053-587X",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

number = "4",

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TY - JOUR

T1 - Nonparametric change detection and estimation in large-scale sensor networks

AU - He, Ting

AU - Ben-David, Shai

AU - Tong, Lang

N1 - Funding Information: Manuscript received December 9, 2004; revised June 14, 2005. This work was supported in part by the Multidisciplinary University Research Initiative (MURI) under the Office of Naval Research Contract N00014-00-1-0564, and Army Research Laboratory CTA on Communication and Networks under Grant DAAD19-01-2-0011, and the National Science Foundation under Contract CCR-0311055. Part of this work was presented at the 38th Annual Conference on Information Sciences and Systems (CISS’04), Princeton, NJ, March 17–19, 2004; the 24th Army Science Conference, Orlando, FL, November 29– December 2, 2004; the 2005 IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP’05), Philadelphia, PA, March 18–23, 2005; and the 2005 IEEE/AFCEA Military Communications Conference (MILCOM’05), Atlantic City, NJ, October 17–20, 2005. The associate editor coordinating the review of this manuscript and approving it for publication was Dr. Chong-Yung Chi.

PY - 2006/4

Y1 - 2006/4

N2 - The problem of detecting changes in the distribution of alarmed sensors is considered. Under a nonparametric change detection framework, several detection and estimation algorithms are presented based on the Vapnik-Chervonenkis (VC) theory. Theoretical performance guarantees are obtained by providing error exponents for false-alarm and miss detection probabilities. Recursive algorithms for the efficient computation of test statistics are derived. The estimation problem is also considered in which, after detection is made, the location with maximum distribution change is estimated.

AB - The problem of detecting changes in the distribution of alarmed sensors is considered. Under a nonparametric change detection framework, several detection and estimation algorithms are presented based on the Vapnik-Chervonenkis (VC) theory. Theoretical performance guarantees are obtained by providing error exponents for false-alarm and miss detection probabilities. Recursive algorithms for the efficient computation of test statistics are derived. The estimation problem is also considered in which, after detection is made, the location with maximum distribution change is estimated.

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U2 - 10.1109/TSP.2006.870635

DO - 10.1109/TSP.2006.870635

M3 - Article

AN - SCOPUS:33645325974

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VL - 54

SP - 1204

EP - 1217

JO - IEEE Transactions on Signal Processing

JF - IEEE Transactions on Signal Processing

IS - 4

ER -

Nonparametric change detection and estimation in large-scale sensor networks

Abstract

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