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  • Research Article
  • Open Access

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

  • 1Email author,
  • 2,
  • 3,
  • 2 and
  • 1
EURASIP Journal on Wireless Communications and Networking20102010:627372

https://doi.org/10.1155/2010/627372

  • Received: 30 October 2009
  • Accepted: 9 May 2010
  • Published:

Abstract

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.

Keywords

  • Time Series
  • Information System
  • Pattern Recognition
  • Temperature Distribution
  • Sensor Network

Publisher note

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Authors’ Affiliations

(1)
Institut Charles Delaunay, Université de Technologie de Troyes, 6279 UMR CNRS, 12 rue Marie Curie, BP2060, 10010 Troyes Cedex, France
(2)
Fizeau Laboratory, Observatoire de la Côte d'Azur, Université de Nice Sophia-Antipolis, 6525 UMR CNRS, 06108 Nice, France
(3)
Department of Electrical Engineering, Federal University of Santa Catarina, 88040-900 Florianópolis, SC, Brazil

Copyright

© 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.

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