TY - JOUR

T1 - Distributed quantization-estimation using wireless sensor networks

AU - Ribeiro, Alejandro

AU - Giannakis, Georgios B

PY - 2005/9/15

Y1 - 2005/9/15

N2 - Wireless sensor networks deployed to perform surveillance and monitoring tasks have to operate under stringent energy and bandwidth limitations. These motivate well distributed estimation scenarios where sensors quantize and transmit only one, or a few bits per observation, for use in forming parameter estimators of interest. In a companion paper, we developed algorithms and studied interesting tradeoffs that emerge even in the simplest distributed setup of estimating a scalar location parameter in the presence of zero-mean additive white Gaussian noise of known variance. Herein, we derive distributed estimators based on binary observations along with their fundamental error-variance limits for more pragmatic signal models: i) known univariate but generally non-Gaussian noise probability density functions (pdfs); ii) known noise pdfs with a finite number of unknown parameters; and iii) practical generalizations to multivariate and possibly correlated pdfs. Estimators utilizing either independent or colored binary observations are developed and analyzed. Corroborating simulations present comparisons with the clairvoyant sample-mean estimator based on unquantized sensor observations, and include a motivating application entailing distributed parameter estimation where a WSN is used for habitat monitoring.

AB - Wireless sensor networks deployed to perform surveillance and monitoring tasks have to operate under stringent energy and bandwidth limitations. These motivate well distributed estimation scenarios where sensors quantize and transmit only one, or a few bits per observation, for use in forming parameter estimators of interest. In a companion paper, we developed algorithms and studied interesting tradeoffs that emerge even in the simplest distributed setup of estimating a scalar location parameter in the presence of zero-mean additive white Gaussian noise of known variance. Herein, we derive distributed estimators based on binary observations along with their fundamental error-variance limits for more pragmatic signal models: i) known univariate but generally non-Gaussian noise probability density functions (pdfs); ii) known noise pdfs with a finite number of unknown parameters; and iii) practical generalizations to multivariate and possibly correlated pdfs. Estimators utilizing either independent or colored binary observations are developed and analyzed. Corroborating simulations present comparisons with the clairvoyant sample-mean estimator based on unquantized sensor observations, and include a motivating application entailing distributed parameter estimation where a WSN is used for habitat monitoring.

KW - (5)Comm/information theory aspects of sensor networks

UR - http://www.scopus.com/inward/record.url?scp=24344490292&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=24344490292&partnerID=8YFLogxK

M3 - Article

AN - SCOPUS:24344490292

SN - 0536-1486

VL - 2

SP - 730

EP - 736

JO - IEEE International Conference on Communications

JF - IEEE International Conference on Communications

M1 - CT17-2

ER -