Next Article in Journal
Line-Monitoring, Hyperspectral Fluorescence Setup for Simultaneous Multi-Analyte Biosensing
Next Article in Special Issue
A Grid-Based Distributed Event Detection Scheme for Wireless Sensor Networks
Previous Article in Journal / Special Issue
Collaborative Localization Algorithms for Wireless Sensor Networks with Reduced Localization Error
Article

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

1
Group of Computer Networks, Software Engineering and Systems (GREat), Federal University of Ceará, CEP 60455-760, Fortaleza, Brazil
2
LRSM/IBISC Laboratory, University of Evry Val d’Essonne, 91020 Evry Courcouronnes CE 1433, France
*
Author to whom correspondence should be addressed.
Sensors 2011, 11(11), 10010-10037; https://doi.org/10.3390/s111110010
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)
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
Show Figures

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. https://doi.org/10.3390/s111110010

AMA Style

Carvalho C, Gomes DG, Agoulmine N, De Souza JN. Improving Prediction Accuracy for WSN Data Reduction by Applying Multivariate Spatio-Temporal Correlation. Sensors. 2011; 11(11):10010-10037. https://doi.org/10.3390/s111110010

Chicago/Turabian Style

Carvalho, Carlos, Danielo G. Gomes, Nazim Agoulmine, and José Neuman De Souza. 2011. "Improving Prediction Accuracy for WSN Data Reduction by Applying Multivariate Spatio-Temporal Correlation" Sensors 11, no. 11: 10010-10037. https://doi.org/10.3390/s111110010

Find Other Styles

Article Access Map by Country/Region

1
Only visits after 24 November 2015 are recorded.
Back to TopTop