A Bayesian Framework for the Automated Online Assessment of Sensor Data Quality
AbstractOnline 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. View Full-Text
Scifeed alert for new publicationsNever miss any articles matching your research from any publisher
- Get alerts for new papers matching your research
- Find out the new papers from selected authors
- Updated daily for 49'000+ journals and 6000+ publishers
- Define your Scifeed now
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.Chicago/Turabian Style
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.