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


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.

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Correspondence to Paul Honeine (EURASIP Member).

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Open Access This article is distributed under the terms of the Creative Commons Attribution 2.0 International License ( ), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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Honeine (EURASIP Member), P., Richard, C., Bermudez, J.C.M. et al. A Decentralized Approach for Nonlinear Prediction of Time Series Data in Sensor Networks. J Wireless Com Network 2010, 627372 (2010).

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