NDLI: Cramer-Rao bound-based adaptive quantization for target tracking in wireless sensor networks

Content Provider	IEEE Xplore Digital Library
Author	Mansouri, M. Ouachani, I. Snoussi, H. Richard, C.
Copyright Year	2009
Description	Author affiliation: CIRTA'COM Sup'Com-Parc Technologique Route de Raoued-TUNISIE (Ouachani, I.) \|\| ICD/M2S, University of Technology of Troyes 12, rue Marie-Curie, 10000, France (Mansouri, M.; Snoussi, H.; Richard, C.)
Abstract	This work deals with the problem of target tracking in wireless sensor networks where the observed system is assumed to evolve according to a probabilistic state space model. We propose to improve the use of the variational filtering (VF) by quantizing the data collected by the sensors to higher levels respecting the tradeoff between the information relevance of sensor measurements and the energy costs. In fact, VF has been shown to be suitable to the communication constraints of sensor networks. Its efficiency relies on the fact that the online update of the filtering distribution and its compression are simultaneously performed. But till now, it has been used only for binary sensor networks. In this paper, we propose an adaptive quantization algorithm taking benefit from the VF properties. At each sampling instant, by minimizing the Cramér-Rao bound, the adaptive quantization technique provides the optimal number of quantization bits per observation. The computation of this criteria is based on the target position predictive distribution provided by the VF algorithm. The simulation results show that the adaptive quantization algorithm, for the same sensor transmitting power, outperforms both the VF algorithm using a fixed optimal quantization level (minimizing the MSE) and the VF algorithm based on binary sensors.
Starting Page	693
Ending Page	696
File Size	172578
Page Count	4
File Format	PDF
ISBN	9781424427093
e-ISBN	9781424427116
DOI	10.1109/SSP.2009.5278482
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2009-08-31
Publisher Place	United Kingdom
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Quantization Target tracking Wireless sensor networks State-space methods Information filtering Information filters Energy measurement Costs Sampling methods Distributed computing
Content Type	Text
Resource Type	Article

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