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A Bayesian Framework for the Automated Online Assessment of Sensor Data Quality
Intelligent Sensing and System Laboratory (ISSL), Commonwealth Science and Industrial Research Organisation (CSIRO), CSIRO Marine and Atmospheric Laboratories, Castray Esplanade, Hobart 7001, Australia
Human Interface Technology Laboratory, University of Tasmania, Launceston 7250, Australia
* Author to whom correspondence should be addressed.
Received: 2 March 2012; in revised form: 3 July 2012 / Accepted: 4 July 2012 / Published: 11 July 2012
Abstract: Online automated quality assessment is critical to determine a sensor’s fitness for purpose in real-time applications. A Dynamic Bayesian Network (DBN) framework is proposed to produce probabilistic quality assessments and represent the uncertainty of sequentially correlated sensor readings. This is a novel framework to represent the causes, quality state and observed effects of individual sensor errors without imposing any constraints upon the physical deployment or measured phenomenon. It represents the casual relationship between quality tests and combines them in a way to generate uncertainty estimates of samples. The DBN was implemented for a particular marine deployment of temperature and conductivity sensors in Hobart, Australia. The DBN was shown to offer a substantial average improvement (34%) in replicating the error bars that were generated by experts when compared to a fuzzy logic approach.
Keywords: online filtering; automated; quality assessment; sensors; dynamic Bayesian networks
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Cite This Article
MDPI and ACS Style
Smith, D.; Timms, G.; De Souza, P.; D’Este, C. A Bayesian Framework for the Automated Online Assessment of Sensor Data Quality. Sensors 2012, 12, 9476-9501.
Smith D, Timms G, De Souza P, D’Este C. A Bayesian Framework for the Automated Online Assessment of Sensor Data Quality. Sensors. 2012; 12(7):9476-9501.
Smith, Daniel; Timms, Greg; De Souza, Paulo; D’Este, Claire. 2012. "A Bayesian Framework for the Automated Online Assessment of Sensor Data Quality." Sensors 12, no. 7: 9476-9501.