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Sensors 2011, 11(11), 10010-10037;

Improving Prediction Accuracy for WSN Data Reduction by Applying Multivariate Spatio-Temporal Correlation

Group of Computer Networks, Software Engineering and Systems (GREat), Federal University of Ceará, CEP 60455-760, Fortaleza, Brazil
LRSM/IBISC Laboratory, University of Evry Val d’Essonne, 91020 Evry Courcouronnes CE 1433, France
Author to whom correspondence should be addressed.
Received: 18 August 2011 / Revised: 13 October 2011 / Accepted: 19 October 2011 / Published: 25 October 2011
(This article belongs to the Special Issue Collaborative Sensors)
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This paper proposes a method based on multivariate spatial and temporal correlation to improve prediction accuracy in data reduction for Wireless Sensor Networks (WSN). Prediction of data not sent to the sink node is a technique used to save energy in WSNs by reducing the amount of data traffic. However, it may not be very accurate. Simulations were made involving simple linear regression and multiple linear regression functions to assess the performance of the proposed method. The results show a higher correlation between gathered inputs when compared to time, which is an independent variable widely used for prediction and forecasting. Prediction accuracy is lower when simple linear regression is used, whereas multiple linear regression is the most accurate one. In addition to that, our proposal outperforms some current solutions by about 50% in humidity prediction and 21% in light prediction. To the best of our knowledge, we believe that we are probably the first to address prediction based on multivariate correlation for WSN data reduction. View Full-Text
Keywords: wireless sensor networks; multivariate correlation; data reduction wireless sensor networks; multivariate correlation; data reduction
This is an open access article distributed under the Creative Commons Attribution License (CC BY 3.0).

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MDPI and ACS Style

Carvalho, C.; Gomes, D.G.; Agoulmine, N.; de Souza, J.N. Improving Prediction Accuracy for WSN Data Reduction by Applying Multivariate Spatio-Temporal Correlation. Sensors 2011, 11, 10010-10037.

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