Document Type

Article

Publication Date

12-2008

Journal or Book Title

IEEE Transactions on Signal Processing

Volume

56

Issue

12

First Page

6069

Last Page

6085

DOI

10.1109/TSP.2008.2005753

Abstract

In large-scale wireless sensor networks, sensor-processor elements (nodes) are densely deployed to monitor the environment; consequently, their observations form a random field that is highly correlated in space. We consider a fusion sensor-network architecture where, due to the bandwidth and energy constraints, the nodes transmit quantized data to a fusion center. The fusion center provides feedback by broadcasting summary information to the nodes. In addition to saving energy, this feedback ensures reliability and robustness to node and fusion-center failures. We assume that the sensor observations follow a linear-regression model with known spatial covariances between any two locations within a region of interest. We propose a Bayesian framework for adaptive quantization, fusion-center feedback, and estimation of the random field and its parameters. We also derive a simple suboptimal scheme for estimating the unknown parameters, apply our estimation approach to the no-feedback scenario, discuss field prediction at arbitrary locations within the region of interest, and present numerical examples demonstrating the performance of the proposed methods.

Comments

This is a post-print of an article from IEEE Transactions on Signal Processing 56 (2008): 6069–6085, doi:10.1109/TSP.2008.2005753. Posted with permission.

Copyright Owner

IEEE

Language

en

File Format

application/pdf

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