A distributed EM algorithm with consensus is proposed for density estimation and clustering using WSNs in the presence of mixtures of Gaussians. The EM algorithm is a general framework for maximum likelihood estimation in hidden variable models, usually implemented in a central node with global information of the network. The average consensus algorithm is a simple robust scheme for computing averages in a distributed manner. In this contribution, we run a distributed EM algorithm where the nodes obtain global knowledge of the statistics through consensus with local information exchange only in a WSN with instantaneous random links. Starting from a set of initial values, the nodes are able to compute the complete statistics of a mixture of Gaussians and classify into clusters according to the sensed density using a simple decision rule. A trade off between power consumption and final accuracy of the estimates is established through simulations. © 2010 IEEE.

Consensus for distributed EM-based clustering in WSNs / S., Silva Pereira; BARBAROSSA, Sergio; Alba Pages, Zamora. - (2010), pp. 45-48. ((Intervento presentato al convegno 2010 IEEE Sensor Array and Multichannel Signal Processing Workshop, SAM 2010 tenutosi a Jerusalem; Israel nel 4 October 2010 through 7 October 2010 [10.1109/sam.2010.5606758].

Consensus for distributed EM-based clustering in WSNs

BARBAROSSA, Sergio;
2010

Abstract

A distributed EM algorithm with consensus is proposed for density estimation and clustering using WSNs in the presence of mixtures of Gaussians. The EM algorithm is a general framework for maximum likelihood estimation in hidden variable models, usually implemented in a central node with global information of the network. The average consensus algorithm is a simple robust scheme for computing averages in a distributed manner. In this contribution, we run a distributed EM algorithm where the nodes obtain global knowledge of the statistics through consensus with local information exchange only in a WSN with instantaneous random links. Starting from a set of initial values, the nodes are able to compute the complete statistics of a mixture of Gaussians and classify into clusters according to the sensed density using a simple decision rule. A trade off between power consumption and final accuracy of the estimates is established through simulations. © 2010 IEEE.
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11573/186094
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