Consider a wireless sensor network (WSN) with a fusion center (FC) deployed to estimate signal parameters from noisy sensor measurements. If the WSN has a large number of low-cost, battery-operated sensor nodes with limited transmission bandwidth, then conservation of transmission resources (power and bandwidth) is paramount. To this end, the present paper develops a novel data reduction method which requires no inter-sensor collaboration and results in only a subset of the sensor measurements transmitted to the FC. Using interval censoring as a data-reduction method, each sensor decides separately whether to censor its acquired measurements based on a rule that promotes censoring of measurements with least impact on the estimator mean-square error (MSE). Leveraging the statistical distribution of sensor data, the censoring mechanism and the received uncensored data, FC-based estimators are derived for both deterministic (via maximum likelihood estimation) and random parameters (via maximum a posteriori probability estimation) for a linear-Gaussian model. Quantization of the uncensored measurements at the sensor nodes offers an additional degree of freedom in the resource conservation versus estimator MSE reduction tradeoff. Cramér-Rao bound analysis for the different censor-estimators and censor-quantizer estimators is also provided to benchmark and facilitate MSE-based performance comparisons. Numerical simulations corroborate the analytical findings and demonstrate that the proposed censoring-estimation approach performs competitively with alternative methods, under different sensing conditions, while having lower computational complexity.
Bibliographical noteFunding Information:
Manuscript received May 21, 2011; revised August 11, 2011; accepted October 01, 2011. Date of publication November 30, 2011; date of current version December 16, 2011. The associate editor coordinating the review of this manuscript and approving it for publication was Dr. Roberto Lopez-Valcarce. This work was supported by the AFOSR MURI Grant FA9550-10-1-0567. Part of this work was presented at Twelfth International Workshop on Signal Processing for Advanced Wireless Communications, San Francisco, California, June 2011, and at the Fourteenth International Conference on Information Fusion, Chicago, Illinois, July 2011.
- Censoring sensors
- decentralized estimation
- sensor fusion
- sensor selection
- wireless sensor networks