A Bayesian Framework for the Automated Online Assessment of Sensor Data Quality

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...

Full description

Bibliographic Details
Main Authors:	Claire D’Este, Daniel Smith, Paulo De Souza, Greg Timms
Format:	Article
Language:	English
Published:	MDPI AG 2012-07-01
Series:	Sensors
Subjects:	online filtering automated quality assessment sensors dynamic Bayesian networks
Online Access:	http://www.mdpi.com/1424-8220/12/7/9476

id	doaj-c59752f62f1848fab6bda3435ba9f468
record_format	Article
spelling	doaj-c59752f62f1848fab6bda3435ba9f4682020-11-25T00:43:32ZengMDPI AGSensors1424-82202012-07-011279476950110.3390/s120709476A Bayesian Framework for the Automated Online Assessment of Sensor Data QualityClaire D’EsteDaniel SmithPaulo De SouzaGreg TimmsOnline 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.http://www.mdpi.com/1424-8220/12/7/9476online filteringautomatedquality assessmentsensorsdynamic Bayesian networks
collection	DOAJ
language	English
format	Article
sources	DOAJ
author	Claire D’Este Daniel Smith Paulo De Souza Greg Timms
spellingShingle	Claire D’Este Daniel Smith Paulo De Souza Greg Timms A Bayesian Framework for the Automated Online Assessment of Sensor Data Quality Sensors online filtering automated quality assessment sensors dynamic Bayesian networks
author_facet	Claire D’Este Daniel Smith Paulo De Souza Greg Timms
author_sort	Claire D’Este
title	A Bayesian Framework for the Automated Online Assessment of Sensor Data Quality
title_short	A Bayesian Framework for the Automated Online Assessment of Sensor Data Quality
title_full	A Bayesian Framework for the Automated Online Assessment of Sensor Data Quality
title_fullStr	A Bayesian Framework for the Automated Online Assessment of Sensor Data Quality
title_full_unstemmed	A Bayesian Framework for the Automated Online Assessment of Sensor Data Quality
title_sort	bayesian framework for the automated online assessment of sensor data quality
publisher	MDPI AG
series	Sensors
issn	1424-8220
publishDate	2012-07-01
description	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.
topic	online filtering automated quality assessment sensors dynamic Bayesian networks
url	http://www.mdpi.com/1424-8220/12/7/9476
work_keys_str_mv	AT clairedeste abayesianframeworkfortheautomatedonlineassessmentofsensordataquality AT danielsmith abayesianframeworkfortheautomatedonlineassessmentofsensordataquality AT paulodesouza abayesianframeworkfortheautomatedonlineassessmentofsensordataquality AT gregtimms abayesianframeworkfortheautomatedonlineassessmentofsensordataquality AT clairedeste bayesianframeworkfortheautomatedonlineassessmentofsensordataquality AT danielsmith bayesianframeworkfortheautomatedonlineassessmentofsensordataquality AT paulodesouza bayesianframeworkfortheautomatedonlineassessmentofsensordataquality AT gregtimms bayesianframeworkfortheautomatedonlineassessmentofsensordataquality
_version_	1725277903867871232

A Bayesian Framework for the Automated Online Assessment of Sensor Data Quality

Similar Items