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

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  • Yongheng Wang
  • Kening Cao

Abstract

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.

Suggested Citation

  • Yongheng Wang & Kening Cao, 2014. "A Proactive Complex Event Processing Method for Large-Scale Transportation Internet of Things," International Journal of Distributed Sensor Networks, , vol. 10(3), pages 159052-1590, March.
    • Handle: RePEc:sae:intdis:v:10:y:2014:i:3:p:159052
    DOI: 10.1155/2014/159052
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