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

Gning, Amadou and Mihaylova, Lyudmila (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, Seattle, USA, pp. 656-663. ISBN 978-0-9824-4380-4

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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.

Item Type:
Contribution in Book/Report/Proceedings
Additional Information:
IEEE Catalog Number: CFP09FUS-CDR ISBN: 978-0-9824438-0-4
Uncontrolled Keywords:
/dk/atira/pure/core/keywords/computingcommunicationsandict
Subjects:
?? belief propagationdistributed inferencedynamic clusteringsensor networksobject trackingcommunication failuresmarkov random fieldscomputing, communications and ictqa76 computer software ??
ID Code:
26755
Deposited By:
Deposited On:
13 Jul 2009 08:40
Refereed?:
Yes
Published?:
Published
Last Modified:
16 Jul 2024 02:39