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Dynamic clustering and belief propagation for distributed inference in random sensor networks with deficient links.

Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSN › Conference contribution/Paper › peer-review

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Dynamic clustering and belief propagation for distributed inference in random sensor networks with deficient links. / Gning, Amadou; Mihaylova, Lyudmila.
12th International Conference on Information Fusion, 2009. FUSION '09. . IEEE, 2009. p. 656-663.

Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSN › Conference contribution/Paper › peer-review

Harvard

Gning, A & Mihaylova, L 2009, Dynamic clustering and belief propagation for distributed inference in random sensor networks with deficient links. in 12th International Conference on Information Fusion, 2009. FUSION '09. . IEEE, pp. 656-663, 12th International Conference on Information Fusion, Seattle, USA, 6/07/09. <http://www.isif.org/fusion2009.htm>

APA

Gning, A., & Mihaylova, L. (2009). Dynamic clustering and belief propagation for distributed inference in random sensor networks with deficient links. In 12th International Conference on Information Fusion, 2009. FUSION '09. (pp. 656-663). IEEE. http://www.isif.org/fusion2009.htm

Vancouver

Gning A, Mihaylova L. Dynamic clustering and belief propagation for distributed inference in random sensor networks with deficient links. In 12th International Conference on Information Fusion, 2009. FUSION '09. . IEEE. 2009. p. 656-663

Author

Gning, Amadou ; Mihaylova, Lyudmila. / Dynamic clustering and belief propagation for distributed inference in random sensor networks with deficient links. 12th International Conference on Information Fusion, 2009. FUSION '09. . IEEE, 2009. pp. 656-663

Bibtex

@inproceedings{d58127ea65df4c98b285251b6fecd03e,
title = "Dynamic clustering and belief propagation for distributed inference in random sensor networks with deficient links.",
abstract = "A fundamental issue in real-world monitoring network systems is the choice of sensors to track local events. Ideally, the sensors work together, in a distributed manner, to achieve a common mission-specific task. This paper develops a framework for distributed inference based on dynamic clustering and belief propagation in sensor networks with deficient links. We investigate this approach for dynamic clustering of sensor nodes combined with belief propagation for the purposes of object tracking in sensor networks with and without deficient links. We demonstrate the efficiency of our approach over an example of hundreds randomly deployed sensors.",
keywords = "Belief propagation, distributed inference, dynamic clustering, sensor networks, object tracking, communication failures, Markov random fields",
author = "Amadou Gning and Lyudmila Mihaylova",
note = "IEEE Catalog Number: CFP09FUS-CDR ISBN: 978-0-9824438-0-4; 12th International Conference on Information Fusion ; Conference date: 06-07-2009 Through 09-07-2009",
year = "2009",
month = jul,
day = "7",
language = "English",
isbn = "978-0-9824-4380-4",
pages = "656--663",
booktitle = "12th International Conference on Information Fusion, 2009. FUSION '09.",
publisher = "IEEE",

}

RIS

TY - GEN

T1 - Dynamic clustering and belief propagation for distributed inference in random sensor networks with deficient links.

AU - Gning, Amadou

AU - Mihaylova, Lyudmila

N1 - IEEE Catalog Number: CFP09FUS-CDR ISBN: 978-0-9824438-0-4

PY - 2009/7/7

Y1 - 2009/7/7

N2 - A fundamental issue in real-world monitoring network systems is the choice of sensors to track local events. Ideally, the sensors work together, in a distributed manner, to achieve a common mission-specific task. This paper develops a framework for distributed inference based on dynamic clustering and belief propagation in sensor networks with deficient links. We investigate this approach for dynamic clustering of sensor nodes combined with belief propagation for the purposes of object tracking in sensor networks with and without deficient links. We demonstrate the efficiency of our approach over an example of hundreds randomly deployed sensors.

AB - A fundamental issue in real-world monitoring network systems is the choice of sensors to track local events. Ideally, the sensors work together, in a distributed manner, to achieve a common mission-specific task. This paper develops a framework for distributed inference based on dynamic clustering and belief propagation in sensor networks with deficient links. We investigate this approach for dynamic clustering of sensor nodes combined with belief propagation for the purposes of object tracking in sensor networks with and without deficient links. We demonstrate the efficiency of our approach over an example of hundreds randomly deployed sensors.

KW - Belief propagation

KW - distributed inference

KW - dynamic clustering

KW - sensor networks

KW - object tracking

KW - communication failures

KW - Markov random fields

M3 - Conference contribution/Paper

SN - 978-0-9824-4380-4

SP - 656

EP - 663

BT - 12th International Conference on Information Fusion, 2009. FUSION '09.

PB - IEEE

T2 - 12th International Conference on Information Fusion

Y2 - 6 July 2009 through 9 July 2009

ER -