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Traffic Prediction over Wireless Networks

Traffic Prediction over Wireless Networks

Huifang Feng, Maode Ma
Copyright: © 2010 |Pages: 26
ISBN13: 9781615207718|ISBN10: 1615207716|EISBN13: 9781615207725
DOI: 10.4018/978-1-61520-771-8.ch005
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MLA

Feng, Huifang, and Maode Ma. "Traffic Prediction over Wireless Networks." Wireless Network Traffic and Quality of Service Support: Trends and Standards, edited by Thomas D. Lagkas, et al., IGI Global, 2010, pp. 87-112. https://doi.org/10.4018/978-1-61520-771-8.ch005

APA

Feng, H. & Ma, M. (2010). Traffic Prediction over Wireless Networks. In T. Lagkas, P. Angelidis, & L. Georgiadis (Eds.), Wireless Network Traffic and Quality of Service Support: Trends and Standards (pp. 87-112). IGI Global. https://doi.org/10.4018/978-1-61520-771-8.ch005

Chicago

Feng, Huifang, and Maode Ma. "Traffic Prediction over Wireless Networks." In Wireless Network Traffic and Quality of Service Support: Trends and Standards, edited by Thomas D. Lagkas, Pantelis Angelidis, and Loukas Georgiadis, 87-112. Hershey, PA: IGI Global, 2010. https://doi.org/10.4018/978-1-61520-771-8.ch005

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Abstract

The predictability of network traffic is an important and widely studied topic because it can lead to the solutions to get more efficient dynamic bandwidth allocation, admission control, congestion control and better performance of wireless networks. In this chapter, firstly, the authors briefly describe a number of traffic models that include time series models, artificial neural networks models, wavelet-based models, and support vector machine-based models. Secondly, they give the prediction method and metrics of measuring the accuracy of a prediction. Finally, they examine the feasibility of applying support vector machine into the prediction of actual traffic in WLANs and evaluate the performance of different prediction models such as ARIMA, FARIMA, and artificial neural network, wavelet-based and support vector machine-based models for the prediction of the real WLANs traffic.

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