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Network Traffic Prediction Based on Deep Belief Network and Spatiotemporal Compressive Sensing in Wireless Mesh Backbone Networks
Joint Authors
Wan, Liang-Tian
Song, Houbing
Wang, Xiaojie
Nie, Laisen
Yu, Shui
Jiang, Dingde
Source
Wireless Communications and Mobile Computing
Issue
Vol. 2018, Issue 2018 (31 Dec. 2018), pp.1-10, 10 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2018-01-04
Country of Publication
Egypt
No. of Pages
10
Main Subjects
Information Technology and Computer Science
Abstract EN
Wireless mesh network is prevalent for providing a decentralized access for users and other intelligent devices.
Meanwhile, it can be employed as the infrastructure of the last few miles connectivity for various network applications, for example, Internet of Things (IoT) and mobile networks.
For a wireless mesh backbone network, it has obtained extensive attention because of its large capacity and low cost.
Network traffic prediction is important for network planning and routing configurations that are implemented to improve the quality of service for users.
This paper proposes a network traffic prediction method based on a deep learning architecture and the Spatiotemporal Compressive Sensing method.
The proposed method first adopts discrete wavelet transform to extract the low-pass component of network traffic that describes the long-range dependence of itself.
Then, a prediction model is built by learning a deep architecture based on the deep belief network from the extracted low-pass component.
Otherwise, for the remaining high-pass component that expresses the gusty and irregular fluctuations of network traffic, the Spatiotemporal Compressive Sensing method is adopted to predict it.
Based on the predictors of two components, we can obtain a predictor of network traffic.
From the simulation, the proposed prediction method outperforms three existing methods.
American Psychological Association (APA)
Nie, Laisen& Wang, Xiaojie& Wan, Liang-Tian& Yu, Shui& Song, Houbing& Jiang, Dingde. 2018. Network Traffic Prediction Based on Deep Belief Network and Spatiotemporal Compressive Sensing in Wireless Mesh Backbone Networks. Wireless Communications and Mobile Computing،Vol. 2018, no. 2018, pp.1-10.
https://search.emarefa.net/detail/BIM-1215720
Modern Language Association (MLA)
Nie, Laisen…[et al.]. Network Traffic Prediction Based on Deep Belief Network and Spatiotemporal Compressive Sensing in Wireless Mesh Backbone Networks. Wireless Communications and Mobile Computing No. 2018 (2018), pp.1-10.
https://search.emarefa.net/detail/BIM-1215720
American Medical Association (AMA)
Nie, Laisen& Wang, Xiaojie& Wan, Liang-Tian& Yu, Shui& Song, Houbing& Jiang, Dingde. Network Traffic Prediction Based on Deep Belief Network and Spatiotemporal Compressive Sensing in Wireless Mesh Backbone Networks. Wireless Communications and Mobile Computing. 2018. Vol. 2018, no. 2018, pp.1-10.
https://search.emarefa.net/detail/BIM-1215720
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1215720