Online Internet Traffic Prediction Models Based on MMSE

An IoT is the communication of sensing devices linked to the Internet in order to communicate data. IoT devices have extremely critical reliability with an efficient and robust network condition. Based on enormous growth in devices and their connectivity, IoT contributes to the bulk of Internet traffic. Prediction of network traffic is very important function of any network. Traffic prediction is important to ensure good system efficiency and ensure service quality of IoT applications, as it relies primarily on congestion management, admission control, allocation of bandwidth to the system, and the identification of anomalies. In this paper, a complete overview of IoT traffic forecasting model using classic time series and artificial neural network is presented. For prediction of IoT traffic, real network traces are used. Prediction models are evaluated using MAE, RMSE, and R -squared values. The experimental results indicate that LSTM- and FNN-based predictive models are highly sensitive and can therefore be used to provide better performance as a timing sequence forecast model than the conventional traffic prediction techniques.

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Assessment of Traffic Prediction Models for Mobile Communication Networks

2019 22nd International Symposium on Wireless Personal Multimedia Communications (WPMC) ◽

10.1109/wpmc48795.2019.9096098 ◽

2019 ◽

Author(s):

Diogo Clemente ◽

Daniel Fernandes ◽

Rodrigo Cortesao ◽

Gabriela Soares ◽

Pedro Sebastiao ◽

...

Keyword(s):

Communication Networks ◽

Mobile Communication ◽

Prediction Models ◽

Traffic Prediction ◽

Mobile Communication Networks

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An Improved LS-SVR Ensemble Learning in Internet Traffic Prediction

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.121-126.3794 ◽

2011 ◽

Vol 121-126 ◽

pp. 3794-3798 ◽

Cited By ~ 1

Author(s):

Kun Lun Li ◽

Ying Hui Ma ◽

Yong Mei Tian ◽

Jing Xie

Keyword(s):

Time Series ◽

Ensemble Learning ◽

Learning Algorithm ◽

Learning Algorithms ◽

Internet Traffic ◽

Traffic Prediction ◽

Initial Training ◽

Classification Problems ◽

Traffic Forecasting ◽

Ensemble Learning Algorithm

In this paper, we present a new method for internet traffic forecasting based on a boosting LS-SVR algorithm. AdaBoost has been proved to be an effective method for improving the performance of weak learning algorithms and widely applied to classification problems. Inspired by it, we use LS-SVR to complete the initial training; and pay more attention on the “high error areas” in the time series; then, we use an ensemble learning algorithm to learn these areas.

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