Traffic prediction for Internet of Things through support vector regression model

With the development of the Internet of Things (IoT), the traffic composition in the network has changed greatly. The traffic analysis is the basis for the further tasks in IoT network, such as intrusion detection, abnormal behavior analysis and attack detection. This paper adopts support vector regression (SVR) to predict traffic data in the wireless sensor networks and IoT network. First, the traffic data is represented as the time series form. Then, the sequence of traffic data is processed by logarithmic function to eliminate the fluctuation of the traffic data. Lastly, the processed traffic sequence data is used to learn a SVR model. The learnt SVR model is used to predict the traffic in the future. The experiments on telemedicine, smart agriculture, vending and automatic driving show that the mean square error of proposed traffic prediction method can achieve less than 0.150.