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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/ProceedingsPaper

Published
Publication date7/07/2009
Host publication12th International Conference on Information Fusion, 2009. FUSION '09.
PublisherIEEE
Pages656-663
Number of pages8
ISBN (Print)978-0-9824-4380-4
<mark>Original language</mark>English
Event12th International Conference on Information Fusion - Seattle, USA

Conference

Conference12th International Conference on Information Fusion
CitySeattle, USA
Period6/07/099/07/09

Conference

Conference12th International Conference on Information Fusion
CitySeattle, USA
Period6/07/099/07/09

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.

Bibliographic note

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