TY - GEN

T1 - A space-time diffusion scheme for peer-to-peer least-squares estimation

AU - Xiao, Lin

AU - Boyd, Stephen

AU - Lall, Sanjay

PY - 2006

Y1 - 2006

N2 - We consider a sensor network in which each sensor takes measurements, at various times, of some unknown parameters, corrupted by independent Gaussian noises. Each node can take a finite or infinite number of measurements, at arbitrary times (i.e., asynchronously). We propose a space-time diffusion scheme, that relies only on peer-to-peer communication, and allows every node to asymptotically compute the global maximum-likelihood estimate of the unknown parameters. At each iteration, information is diffused across the network by a temporal update step and a spatial update step. Both steps update each node's state by a weighted average of its current value and locally available data: new measurements for the time update, and neighbors' data for the spatial update. At any time, any node can compute a local weighted least-squares estimate of the unknown parameters, which converges to the global maximum-likelihood solution. With an infinite number of measurements, these estimates converge to the true parameter values in the sense of mean-square convergence. We show that this scheme is robust to unreliable communication links, and works in a network with dynamically changing topology.

AB - We consider a sensor network in which each sensor takes measurements, at various times, of some unknown parameters, corrupted by independent Gaussian noises. Each node can take a finite or infinite number of measurements, at arbitrary times (i.e., asynchronously). We propose a space-time diffusion scheme, that relies only on peer-to-peer communication, and allows every node to asymptotically compute the global maximum-likelihood estimate of the unknown parameters. At each iteration, information is diffused across the network by a temporal update step and a spatial update step. Both steps update each node's state by a weighted average of its current value and locally available data: new measurements for the time update, and neighbors' data for the spatial update. At any time, any node can compute a local weighted least-squares estimate of the unknown parameters, which converges to the global maximum-likelihood solution. With an infinite number of measurements, these estimates converge to the true parameter values in the sense of mean-square convergence. We show that this scheme is robust to unreliable communication links, and works in a network with dynamically changing topology.

KW - Distributed algorithms

KW - Estimation

KW - Least-squares

KW - Sensor networks

UR - http://www.scopus.com/inward/record.url?scp=34247362474&partnerID=8YFLogxK

U2 - 10.1145/1127777.1127806

DO - 10.1145/1127777.1127806

M3 - Conference contribution

AN - SCOPUS:34247362474

SN - 1595933344

SN - 9781595933348

T3 - Proceedings of the Fifth International Conference on Information Processing in Sensor Networks, IPSN '06

SP - 168

EP - 176

BT - Proceedings of the Fifth International Conference on Information Processing in Sensor Networks, IPSN '06

Y2 - 19 April 2006 through 21 April 2006

ER -