Abstract
Large-scale Internet of Things (IoT) produces enormous events. The key issue in IoT application is how to process the events. In this paper a proactive complex event processing method using parallel Markov Decision Processes is proposed for large-scale IoT. Based on a multi-layered adaptive dynamic Bayesian model, an accurate predictive analytics method is proposed. A parallel Markov decision processes model is designed to support proactive event processing. A state partition method and a reward decomposition method are used to support large-scale application. The experimental evaluations show that this method has good accuracy and scalability when used to process complex event proactively in large-scale internet of things.
This work is supported by the National Natural Science Foundation of China (No.61371116) and the Hunan Provincial Natural Science Foundation (No.13JJ3046).
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Wang, Y., Cao, K. (2014). A Proactive Complex Event Processing Method Based on Parallel Markov Decision Processes. In: Li, F., Li, G., Hwang, Sw., Yao, B., Zhang, Z. (eds) Web-Age Information Management. WAIM 2014. Lecture Notes in Computer Science, vol 8485. Springer, Cham. https://doi.org/10.1007/978-3-319-08010-9_26
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DOI: https://doi.org/10.1007/978-3-319-08010-9_26
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-08009-3
Online ISBN: 978-3-319-08010-9
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