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

  • Bing Liu
  • Fanbo Meng
  • Yun Zhao
  • Xinge Qi
  • Bin Lu
  • Kai Yang
  • Xiao YanEmail author
Conference paper
Part of the Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering book series (LNICST, volume 295)

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 

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Copyright information

© ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering 2019

Authors and Affiliations

  • Bing Liu
    • 1
  • Fanbo Meng
    • 2
  • Yun Zhao
    • 1
  • Xinge Qi
    • 1
  • Bin Lu
    • 2
  • Kai Yang
    • 3
  • Xiao Yan
    • 3
    Email author
  1. 1.State Grid Dalian Electric Power Supply CompanyDalianChina
  2. 2.State Grid Liaoning Electric Power Company LimitedShenyangChina
  3. 3.School of Aeronautics and AstronauticsUESTCChengduChina

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