Predictive Analytics by Using Bayesian Model Averaging for Large-Scale Internet of Things

Abstract

Massive events can be produced today because of the rapid development of the Internet of Things (IoT). Complex event processing, which can be used to extract high-level patterns from raw data, has become an essential part of the IoT middleware. Prediction analytics is an important technology in supporting proactive complex event processing. In this paper, we propose the use of dynamic Bayesian model averaging to develop a high-accuracy prediction analytic method for large-scale IoT application. This method, which is based on a new multilayered adaptive dynamic Bayesian network model, uses Gaussian mixture models and expectation-maximization inference for basic Bayesian prediction. Bayesian model averaging is implemented by using Markov chain Monte Carlo approximation, and a novel dynamic Bayesian model averaging method is proposed based on event context clustering. Simulation experiments show that the proposed prediction analytic method has better accuracy compared to traditional methods. Moreover, the proposed method exhibits acceptable performance when implemented in large-scale IoT applications.