Macro Programming through Bayesian Networks: Distributed Inference and Anomaly Detection

Authors:
Marco Mamei;Radhika Nagpal
Affiliations:
Universita di Modena e Reggio Emilia, Italy;Harvard University, USA
Venue:
PERCOM '07 Proceedings of the Fifth IEEE International Conference on Pervasive Computing and Communications
Year:
2007

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Abstract

Macro programming a distributed system, such as a sensor network, is the ability to specify application tasks at a global level while relying on compiler-like software to translate the global tasks into the individual component activities. Bayesian networks can be regarded as a powerful tool for macro programming a distributed system in a variety of data analysis applications. In this paper we present our architecture to program a sensor network by means of Bayesian networks. We also present some applications developed on a microphone-sensor network, that demonstrate calibration, classification and anomaly detection.