Please use this identifier to cite or link to this item: https://doi.org/10.1109/DCOSS.2013.49
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dc.titleEvent prediction and modeling of variable rate sampled data using dynamic bayesian networks
dc.contributor.authorSharma, V.
dc.contributor.authorTham, C.-K.
dc.date.accessioned2014-10-07T04:44:18Z
dc.date.available2014-10-07T04:44:18Z
dc.date.issued2013
dc.identifier.citationSharma, V., Tham, C.-K. (2013). Event prediction and modeling of variable rate sampled data using dynamic bayesian networks. Proceedings - IEEE International Conference on Distributed Computing in Sensor Systems, DCoSS 2013 : 307-309. ScholarBank@NUS Repository. https://doi.org/10.1109/DCOSS.2013.49
dc.identifier.urihttp://scholarbank.nus.edu.sg/handle/10635/83710
dc.description.abstractEvent detection is an important issue in sensor networks for a variety of real-world applications. Many events in real world are often correlated on a complex spatio-temporal level whereby they are manifested via observations over time and space proximities. In order to predict events in these spatio-temporal observations, the prediction model should be capable of modeling co-dependencies between data observed at various locations. In this paper, we propose a Dynamic Bayesian Network (DBN) with such spatio-temporal event prediction capability in sensor networks deployed for sensing environmental data. More specifically, we develop a DBN model with mixture distribution and a novel learning algorithm, for water level data prediction for different canals, using rainfall data at multiple locations. Experiments on real data demonstrates that our model and training method can provide accurate event prediction in real time for spatio-temporal sensor networks. © 2013 IEEE.
dc.description.urihttp://libproxy1.nus.edu.sg/login?url=http://dx.doi.org/10.1109/DCOSS.2013.49
dc.sourceScopus
dc.subjectDynamic Bayesian Network
dc.subjectEvent modelling and prediction
dc.typeConference Paper
dc.contributor.departmentELECTRICAL & COMPUTER ENGINEERING
dc.description.doi10.1109/DCOSS.2013.49
dc.description.sourcetitleProceedings - IEEE International Conference on Distributed Computing in Sensor Systems, DCoSS 2013
dc.description.page307-309
dc.identifier.isiut000328213800042
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