Open AccessA Proactive Complex Event Processing Method for Large-Scale Transportation Internet of Things
Author(s) -
Yongheng Wang,
Kening Cao
Publication year - 2014
Publication title -
international journal of distributed sensor networks
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.324
H-Index - 53
eISSN - 1550-1477
pISSN - 1550-1329
DOI - 10.1155/2014/159052
Subject(s) - computer science , complex event processing , event (particle physics) , field (mathematics) , scale (ratio) , analytics , key (lock) , the internet , markov decision process , bayesian probability , dynamic bayesian network , bayesian network , process (computing) , internet of things , distributed computing , data mining , machine learning , artificial intelligence , markov process , computer security , world wide web , statistics , physics , mathematics , quantum mechanics , pure mathematics , operating system
The Internet of Things (IoT) provides a new way to improve the transportation system. The key issue is how to process the numerous events generated by IoT. In this paper, a proactive complex event processing method is proposed for large-scale transportation IoT. Based on a multilayered adaptive dynamic Bayesian model, a Bayesian network structure learning algorithm using search-and-score is proposed to support accurate predictive analytics. A parallel Markov decision processes model is designed to support proactive event processing. State partitioning and mean field based approximation are used to support large-scale application. The experimental evaluations show that this method can support proactive complex event processing well in large-scale transportation Internet of Things.
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