[Retracted] Network Traffic Prediction via Deep Graph‐Sequence Spatiotemporal Modeling Based on Mobile Virtual Reality Technology

Abstract

Accurate and real-time network traffic flow forecast holds an important role for network management. Especially at present, virtual reality (VR), artificial intelligence (AI), vehicle-to-everything (V2X), and other technologies are closely combined through the mobile network, which greatly increases the human-computer interaction activities. At the same time, it requires high-throughput, low delay, and high reliable service guarantee. In order to achieve ondemand real-time high-quality network service, we must accurately grasp the dynamic changes of network traffic. However, due to the increase of client mobility and application behavior diversity, the complexity and dynamics of network traffic in the temporal domain and the spatial domain increase sharply. To accurate capture the spatiotemporal features, we propose the spatial-temporal graph convolution gated recurrent unit (GC-GRU) model, which integrates the graph convolutional network (GCN) and the gated recurrent unit (GRU) together. In this model, the GCN structure could handle the spatial features of traffic flow with network topology, and the GRU is used to further process spatiotemporal features. Experiments show that the GC-GRU model has better prediction performance than other baseline models and can obtain spatial-temporal correlation in traffic lows better.

Forecasting Network Interface Flow Using a Broad Learning System Based on the Sparrow Search Algorithm

“…The private traffic dataset is derived from the real incoming traffic data of switch interfaces of an enterprise from 5 October to 18 October 2021. We employ the data from 5 October to 16 On the test set of the public dataset, Figure 5 shows the predicted versus true values of the SSA-BLS model versus the other models. Moreover, to better validate the prediction accuracy of the SSA-BLS model, the model is applied to a private traffic dataset.…”

“…Fang [15] used graph convolutional neural networks and long short-term memory (LSTM) to capture the temporal and spatial aspects of the cellular network of a single cell and build a prediction model, respectively. Zhang [16] proposed a spatio-temporal graph convolutional gated recurrent unit (GC-GRU) model to capture the spatial features of network traffic using graph convolutional neural network (GCN) and further process the spatio-temporal characteristics features using gated units (GRU) to improve the prediction performance of network traffic.…”

Retracted: Network Traffic Prediction via Deep Graph-Sequence Spatiotemporal Modeling Based on Mobile Virtual Reality Technology

“…This article has been retracted by Hindawi, as publisher, following an investigation undertaken by the publisher [1]. This investigation has uncovered evidence of systematic manipulation of the publication and peer-review process.…”