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Title: Decentralized random-field estimation for sensor networks using quantized spatially correlated data and fusion-center feedback
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.
Authors:
;
Award ID(s):
0545571
Publication Date:
NSF-PAR ID:
10013060
Journal Name:
IEEE transactions on signal processing
Volume:
56
Issue:
12
Page Range or eLocation-ID:
6069-6085
ISSN:
1053-587X
Sponsoring Org:
National Science Foundation
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