@inproceedings{1c8a77a425f044159e4290db1de5c203,
title = "Decentralized data selection for MAP estimation: A censoring and quantization approach",
abstract = "A distributed data selection technique for fusion center (FC)-based estimation with a wireless sensor network (WSN) is presented. The data selection is envisioned for a large WSN in which only a subset of measurements need be transmitted to the FC thereby saving on transmission power. Furthermore, quantization of the selected measurements leading to bandwidth savings is also addressed. A novel data selection method using measurement censoring is followed by maximum a posteriori estimation that optimally fuses information from the censored-data model. Censoring and estimation algorithms that are amenable to implementation with WSNs are developed. Bayesian Cram{\'e}r-Rao bound analysis and numerical simulations show that the proposed censoring-based estimator and quantized-censored estimator have competitive (or even superior) mean-square error performance when compared to data selection alternatives under a range of sensing conditions.",
keywords = "Censoring, Data reduction, MAP estimation, Quantization, Sensor selection",
author = "Msechu, {Eric J.} and Giannakis, {Georgios B}",
year = "2011",
language = "English (US)",
isbn = "9781457702679",
series = "Fusion 2011 - 14th International Conference on Information Fusion",
booktitle = "Fusion 2011 - 14th International Conference on Information Fusion",
note = "14th International Conference on Information Fusion, Fusion 2011 ; Conference date: 05-07-2011 Through 08-07-2011",
}