Imputation of missing sensor data values using in-exact replicas

Authors:
Michael W. Bigrigg;H. Scott Matthews;James H. Garrett
Affiliations:
Department of Civil and Environmental Engineering, Carnegie Mellon University, 5000 Forbes Avenue, Pittsburgh, PA 15213, USA.;Department of Civil and Environmental Engineering, Carnegie Mellon University, 5000 Forbes Avenue, Pittsburgh, PA 15213, USA.;Department of Civil and Environmental Engineering, Carnegie Mellon University, 5000 Forbes Avenue, Pittsburgh, PA 15213, USA
Venue:
International Journal of Intelligent Systems Technologies and Applications
Year:
2009

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Abstract

Sensor network systems attempt to exploit redundancy which is assumed to be inherent in a pervasive sensor network due to the density of the sensor nodes. However, there is a lack of sensor data stream correlation between seemingly redundant sensors. The approach presented the uses and prediction techniques that have been used to anticipate a sensor value using its own history, to allow one sensor to be able to anticipate the data from another different sensor, given a historical stream of data from the sensor to be anticipated. The specific mechanisms explored are artificial neural networks, pattern recognition and multivariate polynomial regression.