Abstract
We study the distributed estimations of a spatially correlated random field with decentralized wireless sensor networks (WSNs). Nodes in the WSN take spatial samples of the random field, then each node estimates the values of arbitrary points on the random field by iteratively exchanging information with each other, without the need of a central controller. The objective is to minimize the time (or number of iterations) required for all nodes in the network to reach a distributed consensus on the estimation result, with mean squared error (MSE) below a certain threshold. We find the sufficient conditions for this optimization problem, and identify the asymptotically optimum solutions when time is large and the MSE threshold is small. Specifically, we propose a distributed iterative estimation algorithm that defines the procedures for both information propagation and information estimation in each iteration. The key parameters of the algorithm, including an edge weight matrix and a sample weight matrix, are designed by following the asymptotically optimum criteria. It is shown that the asymptotically optimum performance can be achieved by distributively projecting the measurement samples into a subspace related to the covariance matrices of data and noise samples. Simulation results show that all nodes in a large network can obtain accurate estimation results with only a few iterations.
| Original language | English (US) |
|---|---|
| Title of host publication | 2017 IEEE International Conference on Communications, ICC 2017 |
| Editors | Merouane Debbah, David Gesbert, Abdelhamid Mellouk |
| Publisher | Institute of Electrical and Electronics Engineers Inc. |
| ISBN (Electronic) | 9781467389990 |
| DOIs | |
| State | Published - Jul 28 2017 |
| Event | 2017 IEEE International Conference on Communications, ICC 2017 - Paris, France Duration: May 21 2017 → May 25 2017 |
Other
| Other | 2017 IEEE International Conference on Communications, ICC 2017 |
|---|---|
| Country | France |
| City | Paris |
| Period | 5/21/17 → 5/25/17 |
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All Science Journal Classification (ASJC) codes
- Computer Networks and Communications
- Electrical and Electronic Engineering
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Distributed estimation of a spatially correlated random field in decentralized sensor networks. / Wang, Zuoen; Wu, Jingxian; Yang, Jing.
2017 IEEE International Conference on Communications, ICC 2017. ed. / Merouane Debbah; David Gesbert; Abdelhamid Mellouk. Institute of Electrical and Electronics Engineers Inc., 2017. 7996323.Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
TY - GEN
T1 - Distributed estimation of a spatially correlated random field in decentralized sensor networks
AU - Wang, Zuoen
AU - Wu, Jingxian
AU - Yang, Jing
PY - 2017/7/28
Y1 - 2017/7/28
N2 - We study the distributed estimations of a spatially correlated random field with decentralized wireless sensor networks (WSNs). Nodes in the WSN take spatial samples of the random field, then each node estimates the values of arbitrary points on the random field by iteratively exchanging information with each other, without the need of a central controller. The objective is to minimize the time (or number of iterations) required for all nodes in the network to reach a distributed consensus on the estimation result, with mean squared error (MSE) below a certain threshold. We find the sufficient conditions for this optimization problem, and identify the asymptotically optimum solutions when time is large and the MSE threshold is small. Specifically, we propose a distributed iterative estimation algorithm that defines the procedures for both information propagation and information estimation in each iteration. The key parameters of the algorithm, including an edge weight matrix and a sample weight matrix, are designed by following the asymptotically optimum criteria. It is shown that the asymptotically optimum performance can be achieved by distributively projecting the measurement samples into a subspace related to the covariance matrices of data and noise samples. Simulation results show that all nodes in a large network can obtain accurate estimation results with only a few iterations.
AB - We study the distributed estimations of a spatially correlated random field with decentralized wireless sensor networks (WSNs). Nodes in the WSN take spatial samples of the random field, then each node estimates the values of arbitrary points on the random field by iteratively exchanging information with each other, without the need of a central controller. The objective is to minimize the time (or number of iterations) required for all nodes in the network to reach a distributed consensus on the estimation result, with mean squared error (MSE) below a certain threshold. We find the sufficient conditions for this optimization problem, and identify the asymptotically optimum solutions when time is large and the MSE threshold is small. Specifically, we propose a distributed iterative estimation algorithm that defines the procedures for both information propagation and information estimation in each iteration. The key parameters of the algorithm, including an edge weight matrix and a sample weight matrix, are designed by following the asymptotically optimum criteria. It is shown that the asymptotically optimum performance can be achieved by distributively projecting the measurement samples into a subspace related to the covariance matrices of data and noise samples. Simulation results show that all nodes in a large network can obtain accurate estimation results with only a few iterations.
UR - http://www.scopus.com/inward/record.url?scp=85028313369&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85028313369&partnerID=8YFLogxK
U2 - 10.1109/ICC.2017.7996323
DO - 10.1109/ICC.2017.7996323
M3 - Conference contribution
AN - SCOPUS:85028313369
BT - 2017 IEEE International Conference on Communications, ICC 2017
A2 - Debbah, Merouane
A2 - Gesbert, David
A2 - Mellouk, Abdelhamid
PB - Institute of Electrical and Electronics Engineers Inc.
ER -