A Proactive Complex Event Processing Method for Large-Scale Transportation Internet of Things
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 dyna...
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| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2014-03-01
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| Series: | International Journal of Distributed Sensor Networks |
| Online Access: | https://doi.org/10.1155/2014/159052 |
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| Summary: | 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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| ISSN: | 1550-1477 |