In most sensor network applications, the vector containing the observations gathered by the sensors lies in a space of dimension equal to the number of nodes, typically because of observation noise, even though the useful signal belongs to a subspace of much smaller dimension. This motivates smoothing or rank reduction. We formulate a convex optimization problem, where we incorporate a fidelity constraint that prevents the final smoothed estimate from diverging too far from the observations. This leads to a distributed algorithm in which nodes exchange updates only with neighboring nodes. We show that the widely studied consensus algorithm is indeed only a very specific case of our more general formulation. Finally, we study the convergence rate and propose some approaches to maximize it. ©2008 IEEE.

Globally optimal decentralized spatial smoothing for wireless sensor networks with local interactions / BARBAROSSA, Sergio; T., Battisti; A., Swami. - (2008), pp. 2265-2268. ((Intervento presentato al convegno 2008 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP tenutosi a Las Vegas; United States nel 31 March 2008 through 4 April 2008 [10.1109/icassp.2008.4518097].

Globally optimal decentralized spatial smoothing for wireless sensor networks with local interactions

BARBAROSSA, Sergio;
2008

Abstract

In most sensor network applications, the vector containing the observations gathered by the sensors lies in a space of dimension equal to the number of nodes, typically because of observation noise, even though the useful signal belongs to a subspace of much smaller dimension. This motivates smoothing or rank reduction. We formulate a convex optimization problem, where we incorporate a fidelity constraint that prevents the final smoothed estimate from diverging too far from the observations. This leads to a distributed algorithm in which nodes exchange updates only with neighboring nodes. We show that the widely studied consensus algorithm is indeed only a very specific case of our more general formulation. Finally, we study the convergence rate and propose some approaches to maximize it. ©2008 IEEE.
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11573/186062
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