Adaptive model selection for time series prediction in wireless sensor networks

Authors:
Yann-Aël Le Borgne;Silvia Santini;Gianluca Bontempi
Affiliations:
ULB Machine Learning Group, Department of Computer Science, Université Libre de Bruxelles (U.L.B.), 1050 Brussels, Belgium;Department of Computer Science, Institute for Pervasive Computing, ETH Zurich, ETH-Zentrum, IFW D41.2, CH-8092 Zurich, Switzerland;ULB Machine Learning Group, Department of Computer Science, Université Libre de Bruxelles (U.L.B.), 1050 Brussels, Belgium
Venue:
Signal Processing
Year:
2007

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Abstract

In many practical applications of wireless sensor networks, the sensor nodes are required to report approximations of their readings at regular time intervals. For these applications, it has been shown that time series prediction techniques provide an effective way to reduce the communication effort while guaranteeing user-specified accuracy requirements on collected data. Achievable communication savings offered by time series prediction, however, strongly depend on the type of signal sensed, and in practice an inadequate a priori choice of a prediction model can lead to poor prediction performances. We propose in this paper the adaptive model selection algorithm, a lightweight, online algorithm that allows sensor nodes to autonomously determine a statistically good performing model among a set of candidate models. Experimental results obtained on the basis of 14 real-world sensor time series demonstrate the efficiency and versatility of the proposed framework in improving the communication savings.