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 -