{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,27]],"date-time":"2026-01-27T12:13:20Z","timestamp":1769516000938,"version":"3.49.0"},"reference-count":44,"publisher":"MDPI AG","issue":"7","license":[{"start":{"date-parts":[[2012,7,11]],"date-time":"2012-07-11T00:00:00Z","timestamp":1341964800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/3.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Online automated quality assessment is critical to determine a sensor\u2019s 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.<\/jats:p>","DOI":"10.3390\/s120709476","type":"journal-article","created":{"date-parts":[[2012,7,11]],"date-time":"2012-07-11T11:03:00Z","timestamp":1342004580000},"page":"9476-9501","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":22,"title":["A Bayesian Framework for the Automated Online Assessment of Sensor Data Quality"],"prefix":"10.3390","volume":"12","author":[{"given":"Daniel","family":"Smith","sequence":"first","affiliation":[{"name":"Intelligent Sensing and System Laboratory (ISSL), Commonwealth Science and Industrial Research Organisation (CSIRO), CSIRO Marine and Atmospheric Laboratories, Castray Esplanade, Hobart 7001, Australia"}]},{"given":"Greg","family":"Timms","sequence":"additional","affiliation":[{"name":"Intelligent Sensing and System Laboratory (ISSL), Commonwealth Science and Industrial Research Organisation (CSIRO), CSIRO Marine and Atmospheric Laboratories, Castray Esplanade, Hobart 7001, Australia"}]},{"given":"Paulo","family":"De Souza","sequence":"additional","affiliation":[{"name":"Human Interface Technology Laboratory, University of Tasmania, Launceston 7250, Australia"}]},{"given":"Claire","family":"D\u2019Este","sequence":"additional","affiliation":[{"name":"Intelligent Sensing and System Laboratory (ISSL), Commonwealth Science and Industrial Research Organisation (CSIRO), CSIRO Marine and Atmospheric Laboratories, Castray Esplanade, Hobart 7001, Australia"}]}],"member":"1968","published-online":{"date-parts":[[2012,7,11]]},"reference":[{"key":"ref_1","unstructured":"The Committee on Science Engineering and Public Policy (2009). Ensuring the Integrity, Accessibility, and Stewardship of Research Data in the Digital Age, National Academy of Sciences. Technical Report."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"9","DOI":"10.1007\/978-3-540-75488-6_2","article-title":"Machine learning in ecosystem informatics","volume":"4755\/2007","author":"Dietterich","year":"2007","journal-title":"Lect. Notes Comput. Sci."},{"key":"ref_3","unstructured":"National Science Foundation (NSF) (2005). Sensors for Environmental Observatories: Report of the NSF Sponsored Workshop, NSF. Technical Report."},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Hill, D.J., Minsker, B.S., and Amir, E. (2009). Real-time Bayesian anomaly detection in streaming environmental data. Water Resour. Res., 45.","DOI":"10.1029\/2008WR006956"},{"key":"ref_5","unstructured":"Isaac, D., and Lynes, C. (2003). Automated Data Quality Assessment in the Intelligent Archive, Intelligent Data Understanding, NASA. Technical Report."},{"key":"ref_6","unstructured":"Gupchup, J., Sharma, A., Terzis, A., Burns, R., and Szalay, A. (2008, January 14). The Perils of Detecting Measurement Faults in Environmental Monitoring Networks. Santorini Island, Greece."},{"key":"ref_7","unstructured":"Elnahrawy, E., and Nath, B. (2004, January 19\u201321). Context-Aware Sensors. Berlin, Germany. Volume 2920."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"29","DOI":"10.1145\/1525856.1525863","article-title":"Sensor network data fault types","volume":"5","author":"Ni","year":"2009","journal-title":"ACM Trans. Sens. Netw."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"93","DOI":"10.1109\/TSMCA.2004.838454","article-title":"Active affective state detection and user assistance with dynamic bayesian networks","volume":"35","author":"Li","year":"2005","journal-title":"IEEE Trans. Syst. Man Cybern. Part A Syst. Hum."},{"key":"ref_10","unstructured":"Han, P., Mu, R., and Cui, N. (August, January 9\u2013). Active and Dynamic Multi-Sensor Information Fusion Method based on Dynamic Bayesian Networks. Piscataway, NJ, USA."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"467","DOI":"10.1109\/TSMCB.2005.859081","article-title":"Active and dynamic information fusion for multisensor systems with dynamic networks","volume":"36","author":"Zhang","year":"2006","journal-title":"IEEE Trans. Syst. Man Cybern."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"483","DOI":"10.1016\/j.artint.2007.09.004","article-title":"Quantifying the uncertainty of a belief net response: Bayesian error-bars for belief net inference","volume":"172","author":"Allen","year":"2008","journal-title":"Aritifical Intell."},{"key":"ref_13","unstructured":"Hooper, P., Abbasi-Yadkori, Y., Greiner, R., and Hoehn, B. (2009, January 18\u201321). Improved Mean and Variance Approximations for Belief Net Responses via Network Doubling. Montreal, Canada."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"9589","DOI":"10.3390\/s111009589","article-title":"Automated data quality assessment of marine sensors","volume":"11","author":"Timms","year":"2011","journal-title":"Sensors"},{"key":"ref_15","unstructured":"Smith, D., Timms, G., and DeSouza, P. (September, January 19\u2013). A Quality Control Framework for Marine Sensing Using Statistical, Causal Inference. Waikoloa, HI, USA."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"142","DOI":"10.1111\/j.1467-8640.1989.tb00324.x","article-title":"A model for reasoning about persistence and causation","volume":"5","author":"Dean","year":"1989","journal-title":"Comput. Intell."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"1014","DOI":"10.1016\/j.envsoft.2009.08.010","article-title":"Anomaly detection in streaming environmental sensor data: A data-driven modeling approach","volume":"25","author":"Hill","year":"2010","journal-title":"Environ. Model. Softw."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"1059","DOI":"10.1016\/j.peva.2010.08.018","article-title":"Online anomaly detection for sensor systems: A simple and efficient approach","volume":"67","author":"Yao","year":"2010","journal-title":"Perform. Eval."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Bettencourt, L., Hagberg, A., and Larkey, L. (2007). Separating the wheat from the chaff: Practical anomaly detection schemes in ecological applications of distributed sensor networks. Lect. Notes Comput. Sci.","DOI":"10.1007\/978-3-540-73090-3_15"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"234","DOI":"10.1016\/j.oceaneng.2006.01.011","article-title":"Data quality check procedures of an operational coastal ocean monitoring network","volume":"34","author":"Doonga","year":"2007","journal-title":"Ocean Eng."},{"key":"ref_21","unstructured":"Ramanathan, N., Balzano, L., Burt, M., Estrin, D., Harmon, T., Harvey, C., Jay, J., Kohler, E., Rothenberg, E., and Srivastava, M. (2006). Rapid deployment with confidence: Calibration and fault detection in environmental sensor networks, Centre for Embedded Network Systems, University of California. Technical Report 62, CENS Techical Report."},{"key":"ref_22","unstructured":"Wong, A., Keeley, R.T., and Carval, E.A. (2009). Argo Quality Control Manual, ARGO. [Version 2.5 ed.]."},{"key":"ref_23","unstructured":"Morello, E.B., Lynch, T.P., Slawinski, D., Howell, B., Hughes, D., and Timms, G.P. (September, January 19\u2013). Quantitative Quality Control (QC) procedures for the Australian National Reference Stations: Sensor Data. Waikoloa, HI, USA."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"39","DOI":"10.1145\/1754414.1754419","article-title":"Sensor faults: Detection methods and prevalence in real-world datasets","volume":"6","author":"Sharma","year":"2010","journal-title":"ACM Trans. Sens. Netw."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"159","DOI":"10.1109\/SURV.2010.021510.00088","article-title":"Outlier detection techniques for wireless sensor networks: A survey","volume":"12","author":"Zhang","year":"2010","journal-title":"IEEE Commun. Surv. Tutor."},{"key":"ref_26","unstructured":"Zhang, Z., Guo, S., and He, T. (2009, January 4\u20136). FIND: Faulty Node Detection for Wireless Sensor Networks. Berkeley, CA, USA."},{"key":"ref_27","unstructured":"Mengshoel, O.J., Darwiche, A., and Uckun, S. (2008, January 27). Sensor Validation using Bayesian Networks. Los Angeles, CA, USA."},{"key":"ref_28","unstructured":"Nicholson, A., and Brady, J. (1992, January 17\u201319). Sensor Validation using Bayesian Networks. Stanford, CA, USA. UAI'92."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"1787","DOI":"10.1002\/aic.690490716","article-title":"Probabilistic model for sensor fault detection and identication","volume":"49","author":"Mehranbod","year":"2003","journal-title":"AIChE J."},{"key":"ref_30","unstructured":"Koziana, J., Olson, J., Anselmo, T., and Lu, W. (September, January 15\u2013). Automated Data Quality Assurance for Marine Observations. Quebec, QC, Canada. Volume 1."},{"key":"ref_31","unstructured":"Pearl, J. (1988). Probabilistic Reasoning in Intelligent Systems, Morgan and Kaufman."},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Pourret, O. (2008). Bayesian Networks A Practical Guide to Applications, Wiley. Chapter Introduction to Bayesian Networks.","DOI":"10.1002\/9780470994559"},{"key":"ref_33","unstructured":"Murphy, K. (2002). Dynamic Bayesian Networks: Representation, Inference and Learning. [PhD Thesis, Computer Science Department, University of Berkeley]."},{"key":"ref_34","unstructured":"Heckerman, D. A (1996). Tutorial on Learning With Bayesian Networks, Microsoft Research. Technical Report."},{"key":"ref_35","unstructured":"Tolle, G., Polastre, J., Szewczyk, R., Culler, D., Turner, N., Tu, K., Burgess, S., Dawson, T., Buonadonna, P., Gay, D., and Hong, W. (, January November). A Macroscope in the Redwoods. San Diego, CA, USA."},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Delauney, L., Compere, C., and Lehaitre, M. (2010). Biofouling protection for marine environmental sensors. Ocean Sci.","DOI":"10.5194\/osd-6-2993-2009"},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"1231","DOI":"10.1109\/72.536317","article-title":"Input\/output HMMs for sequence processing","volume":"7","author":"Bengio","year":"1995","journal-title":"IEEE Trans. Neural Netw."},{"key":"ref_38","unstructured":"Hugo, D., Howell, B., D'Este, C., Timms, G., Sharman, C., de Souza, P., and Allen, S. (September, January 19-). Low-cost Marine Monitoring: from Sensors to Information Delivery. Waikoloa, HI, USA."},{"key":"ref_39","unstructured":"Dunbabin, M., Roberts, J., Usher, K., Winstanley, G., and Corke, P. (April, January 18\u2013). A Hybrid AUV Design for Shallow Water Reef Navigation. Barcelona, Spain."},{"key":"ref_40","unstructured":"Intergovernmental Oceanographic Commission and Commission of the European Communities (1993). Manual of Quality Control Procedures for Validation of Oceanographic Data. Manual Guides 26, UNESCO."},{"key":"ref_41","unstructured":"Herzfeld, M., Parslow, J., Margvelashvili, N., Andrewartha, J., and Sakov, P. (2005). Numerical hydrodynamic modelling of the Derwent Estuary. Report prepared for Derwent estuary WQIP. CSIRO Marine Res., 91."},{"key":"ref_42","unstructured":"Fofonoff, N., and Millard, R. (1983). Unesco technical papers in marine science, UNESCO\/SCOR\/ICES\/IAPSO Joint Panel on Oceanographic Tables and Standards."},{"key":"ref_43","unstructured":"Murphy, K. Bayes Network Toolbox for Matlab. Available online: http:\/\/code.google.com\/p\/bnt\/ (accessed on 14 January 2012), version 1.0.7."},{"key":"ref_44","unstructured":"D'Este, C., Terhorst, A., Timms, G., McCulloch, J., and Sharman, C. (September, January 25-). Adaptive Marine Monitoring via Sensor Web Enablement. San Francisco, CA, USA."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/12\/7\/9476\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T21:51:14Z","timestamp":1760219474000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/12\/7\/9476"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2012,7,11]]},"references-count":44,"journal-issue":{"issue":"7","published-online":{"date-parts":[[2012,7]]}},"alternative-id":["s120709476"],"URL":"https:\/\/doi.org\/10.3390\/s120709476","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2012,7,11]]}}}