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.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.