Abstract
Complex Event Processing (CEP) has become the key part of Internet of Things (IoT). Proactive CEP can predict future system states and execute some actions to avoid unwanted states which brings new hope to transportation IoT. In this paper, we propose a proactive CEP architecture and method for transportation IoT. Based on basic CEP technology, this method uses structure varying Bayesian network to predict future events and system states. Different Bayesian network structures are learned and used according to different event context. A networked distributed Markov decision processes model with predicting states is proposed as sequential decision model. Q-learning method is investigated for this model to find optimal joint policy. The experimental evaluations show that this method works well when used to control congestion in transportation IoT.
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