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
Full text not available from this repository.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.