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Open Access

A Decentralized Approach for Nonlinear Prediction of Time Series Data in Sensor Networks

  • Paul Honeine (EURASIP Member)1Email author,
  • Cédric Richard2,
  • José Carlos M. Bermudez3,
  • Jie Chen2 and
  • Hichem Snoussi1
EURASIP Journal on Wireless Communications and Networking20102010:627372

Received: 30 October 2009

Accepted: 9 May 2010

Published: 10 June 2010


Wireless sensor networks rely on sensor devices deployed in an environment to support sensing and monitoring, including temperature, humidity, motion, and acoustic. Here, we propose a new approach to model physical phenomena and track their evolution by taking advantage of the recent developments of pattern recognition for nonlinear functional learning. These methods are, however, not suitable for distributed learning in sensor networks as the order of models scales linearly with the number of deployed sensors and measurements. In order to circumvent this drawback, we propose to design reduced order models by using an easy to compute sparsification criterion. We also propose a kernel-based least-mean-square algorithm for updating the model parameters using data collected by each sensor. The relevance of our approach is illustrated by two applications that consist of estimating a temperature distribution and tracking its evolution over time.


Time SeriesInformation SystemPattern RecognitionTemperature DistributionSensor Network

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Authors’ Affiliations

Institut Charles Delaunay, Université de Technologie de Troyes, 6279 UMR CNRS, Troyes Cedex, France
Fizeau Laboratory, Observatoire de la Côte d'Azur, Université de Nice Sophia-Antipolis, 6525 UMR CNRS, Nice, France
Department of Electrical Engineering, Federal University of Santa Catarina, Florianópolis, Brazil


© Paul Honeine et al. 2010

This article is published under license to BioMed Central Ltd. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.