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...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2011-10-01
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| Series: | Sensors |
| Subjects: | |
| Online Access: | http://www.mdpi.com/1424-8220/11/11/10010/ |
| _version_ | 1811305064248115200 |
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| author | José Neuman de Souza Danielo G. Gomes Nazim Agoulmine Carlos Carvalho |
| author_facet | José Neuman de Souza Danielo G. Gomes Nazim Agoulmine Carlos Carvalho |
| author_sort | José Neuman de Souza |
| collection | DOAJ |
| 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. |
| first_indexed | 2024-04-13T08:19:45Z |
| format | Article |
| id | doaj.art-09e066e55dd84b379c3eb8acec916046 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-04-13T08:19:45Z |
| publishDate | 2011-10-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-09e066e55dd84b379c3eb8acec9160462022-12-22T02:54:41ZengMDPI AGSensors1424-82202011-10-011111100101003710.3390/s111110010Improving Prediction Accuracy for WSN Data Reduction by Applying Multivariate Spatio-Temporal CorrelationJosé Neuman de SouzaDanielo G. GomesNazim AgoulmineCarlos CarvalhoThis 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.http://www.mdpi.com/1424-8220/11/11/10010/wireless sensor networksmultivariate correlationdata reduction |
| spellingShingle | José Neuman de Souza Danielo G. Gomes Nazim Agoulmine Carlos Carvalho Improving Prediction Accuracy for WSN Data Reduction by Applying Multivariate Spatio-Temporal Correlation Sensors wireless sensor networks multivariate correlation data reduction |
| 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 | wireless sensor networks multivariate correlation data reduction |
| url | http://www.mdpi.com/1424-8220/11/11/10010/ |
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