eCite Digital Repository

A Bayesian framework for the automated online assessment of sensor data quality


Smith, D and Timms, G and de Souza, P and D'Este, C, A Bayesian framework for the automated online assessment of sensor data quality, Sensors, 12, (7) pp. 9476-9501. ISSN 1424-8220 (2012) [Refereed Article]


Copyright Statement

Licensed under Creative Commons Attribution 3.0 Unported (CC BY 3.0)

DOI: doi:10.3390/s120709476


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.

Item Details

Item Type:Refereed Article
Keywords:online filtering, automated, quality assessment, sensors, dynamic Bayesian networks
Research Division:Information and Computing Sciences
Research Group:Information systems
Research Field:Information systems not elsewhere classified
Objective Division:Environmental Management
Objective Group:Terrestrial systems and management
Objective Field:Assessment and management of terrestrial ecosystems
UTAS Author:de Souza, P (Professor Paulo de Souza Junior)
ID Code:78644
Year Published:2012
Web of Science® Times Cited:18
Deposited By:Information and Communication Technology
Deposited On:2012-07-13
Last Modified:2017-11-20
Downloads:528 View Download Statistics

Repository Staff Only: item control page