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Improving Prediction Accuracy for WSN Data Reduction by Applying Multivariate Spatio-Temporal Correlation
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. How...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Molecular Diversity Preservation International (MDPI)
2011
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3274268/ https://www.ncbi.nlm.nih.gov/pubmed/22346626 http://dx.doi.org/10.3390/s111110010 |
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author | Carvalho, Carlos Gomes, Danielo G. Agoulmine, Nazim de Souza, José Neuman |
author_facet | Carvalho, Carlos Gomes, Danielo G. Agoulmine, Nazim de Souza, José Neuman |
author_sort | Carvalho, Carlos |
collection | PubMed |
description | 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. |
format | Online Article Text |
id | pubmed-3274268 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2011 |
publisher | Molecular Diversity Preservation International (MDPI) |
record_format | MEDLINE/PubMed |
spelling | pubmed-32742682012-02-15 Improving Prediction Accuracy for WSN Data Reduction by Applying Multivariate Spatio-Temporal Correlation Carvalho, Carlos Gomes, Danielo G. Agoulmine, Nazim de Souza, José Neuman Sensors (Basel) Article 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. Molecular Diversity Preservation International (MDPI) 2011-10-25 /pmc/articles/PMC3274268/ /pubmed/22346626 http://dx.doi.org/10.3390/s111110010 Text en © 2011 by the authors; licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution license (http://creativecommons.org/licenses/by/3.0/). |
spellingShingle | Article Carvalho, Carlos Gomes, Danielo G. Agoulmine, Nazim de Souza, José Neuman Improving Prediction Accuracy for WSN Data Reduction by Applying Multivariate Spatio-Temporal Correlation |
title | Improving Prediction Accuracy for WSN Data Reduction by Applying Multivariate Spatio-Temporal Correlation |
title_full | Improving Prediction Accuracy for WSN Data Reduction by Applying Multivariate Spatio-Temporal Correlation |
title_fullStr | Improving Prediction Accuracy for WSN Data Reduction by Applying Multivariate Spatio-Temporal Correlation |
title_full_unstemmed | Improving Prediction Accuracy for WSN Data Reduction by Applying Multivariate Spatio-Temporal Correlation |
title_short | Improving Prediction Accuracy for WSN Data Reduction by Applying Multivariate Spatio-Temporal Correlation |
title_sort | improving prediction accuracy for wsn data reduction by applying multivariate spatio-temporal correlation |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3274268/ https://www.ncbi.nlm.nih.gov/pubmed/22346626 http://dx.doi.org/10.3390/s111110010 |
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