A Proactive Complex Event Processing Method for Large-Scale Transportation Internet of Things

被引：11

作者：

Wang, Yongheng ^{[1
]}

Cao, Kening ^{[1
]}

机构：

[1] Hunan Univ, Coll Informat Sci & Engn, Changsha 410082, Hunan, Peoples R China

来源：

INTERNATIONAL JOURNAL OF DISTRIBUTED SENSOR NETWORKS | 2014年

基金：

中国国家自然科学基金;

关键词：

Bayesian networks - Markov processes - Processing - Complex networks - Predictive analytics;

D O I：

10.1155/2014/159052

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

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.

引用

页数：8

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