Decentralized random-field estimation for sensor networks
using quantized spatially correlated data and fusion-center feedback
A. Dogandžić and K. Qiu
IEEE Trans. Signal Processing, vol. 56, pp. 6069-6085, Dec. 2008.
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.
Matlab code download: (831 KB)
Here is the code for reproducing the results reported in this paper. Please read the enclosed "Readme" file as well. If you use this code in your research and publications, please refer to the above paper.