Qoe-Based Big Data Analysis With Deep Learning In Pervasive Edge Environment

In the age of big data, the services in pervasive edge environment are expected to offer end-users better Quality-of-Experience (QoE) than that in a normal edge environment. Nevertheless, various types of edge devices with storage, delivery, and sensing are coming into our environment and produce the high-dimensional big data accompanied by a volume of pervasive big data increasingly with a lot of redundancy. Therefore, the satisfaction of QoE becomes the primary challenge in high-dimensional big data on the basis of pervasive edge environment. In this paper, we first propose a QoE model to evaluate the quality of service in pervasive edge environment. The value of QoE does not only include the accurate data, but also the transmission rate. Then, on the basis of the accuracy, we propose a Tensor-Fast Convolutional Neural Network (TF-CNN) algorithm based on Deep Learning, which is suitable for pervasive edge environment with high-dimensional big data analysis. Simulation results reveal that our proposals could achieve high QoE performance.