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

Carlos Carvalho (1) , Danielo G. Gomes (1) , Nazim Agoulmine (2) , José Neuman de Souza (1)

1 GREat - Group of Computer Networks, Software Engineering and Systems
2 IBISC - Informatique, Biologie Intégrative et Systèmes Complexes

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.

Keywords: wireless sensor networks; multivariate correlation; data reduction

Carlos Carvalho, Danielo G. Gomes, Nazim Agoulmine, José Neuman de Souza. Improving Prediction Accuracy for WSN Data Reduction by Applying Multivariate Spatio-Temporal Correlation. Sensors, 2011, 11 (11), pp.10010--10037. ⟨10.3390/s111110010⟩. ⟨hal-00667572⟩