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Simulation Tools and Techniques. 11th International Conference, SIMUtools 2019, Chengdu, China, July 8–10, 2019, Proceedings

Research Article

A Linear Regression-Based Prediction Method to Traffic Flow for Low-Power WAN with Smart Electric Power Allocations

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  • @INPROCEEDINGS{10.1007/978-3-030-32216-8_12,
        author={Bing Liu and Fanbo Meng and Yun Zhao and Xinge Qi and Bin Lu and Kai Yang and Xiao Yan},
        title={A Linear Regression-Based Prediction Method to Traffic Flow for Low-Power WAN with Smart Electric Power Allocations},
        proceedings={Simulation Tools and Techniques. 11th International Conference, SIMUtools 2019, Chengdu, China, July 8--10, 2019, Proceedings},
        proceedings_a={SIMUTOOLS},
        year={2019},
        month={10},
        keywords={Network traffic Low-power WAN Linear regression Traffic modeling Traffic prediction},
        doi={10.1007/978-3-030-32216-8_12}
    }
    
  • Bing Liu
    Fanbo Meng
    Yun Zhao
    Xinge Qi
    Bin Lu
    Kai Yang
    Xiao Yan
    Year: 2019
    A Linear Regression-Based Prediction Method to Traffic Flow for Low-Power WAN with Smart Electric Power Allocations
    SIMUTOOLS
    Springer
    DOI: 10.1007/978-3-030-32216-8_12
Bing Liu1, Fanbo Meng2,*, Yun Zhao1, Xinge Qi1, Bin Lu2, Kai Yang3, Xiao Yan3,*
  • 1: State Grid Dalian Electric Power Supply Company
  • 2: State Grid Liaoning Electric Power Company Limited
  • 3: UESTC
*Contact email: amengfb@163.com, yanxiao@uestc.edu.cn

Abstract

Currently power telecommunication access networks have many new requirements to meet the low-power WAN with smart electric power allocations. In such a case, network traffic in the low-power WAN has exhibited new features and there are some challenges for network managements. This paper uses the linear regression model to propose a new method to model and predict network traffic. Firstly, network traffic is modeled as a linear regression model according to the regression model theory. Then the linear regression modeling method is used to capture network traffic features. By calculating the parameters of the model, it can be decided correctly. Then, we can predict network traffic accurately. Simulation results show that our approach is effective and promising.

Keywords
Network traffic Low-power WAN Linear regression Traffic modeling Traffic prediction
Published
2019-10-24
Appears in
SpringerLink
http://dx.doi.org/10.1007/978-3-030-32216-8_12
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