A broad learning-driven network traffic analysis system based on fog computing paradigm

The development of communication technologies which support traffic-intensive applications presents new challenges in designing a real-time traffic analysis architecture and an accurate method that suitable for a wide variety of traffic types. Current traffic analysis methods are executed on the cloud, which needs to upload the traffic data. Fog computing is a more promising way to save bandwidth resources by offloading these tasks to the fog nodes. However, traffic analysis models based on traditional machine learning need to retrain all traffic data when updating the trained model, which are not suitable for fog computing due to the poor computing power. In this study, we design a novel fog computing based traffic analysis system using broad learning. For one thing, fog computing can provide a distributed architecture for saving the bandwidth resources. For another, we use the broad learning to incrementally train the traffic data, which is more suitable for fog computing because it can support incremental updates of models without retraining all data. We implement our system on the Raspberry Pi, and experimental results show that we have a 98% probability to accurately identify these traffic data. Moreover, our method has a faster training speed compared with Convolutional Neural Network (CNN).