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)


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.


Network traffic Low-power WAN Linear regression Traffic modeling Traffic prediction 


  1. 1.
    Jiang, D., Xu, Z., Chen, Z., et al.: Joint time-frequency sparse estimation of large-scale network traffic. Comput. Netw. 55(10), 3533–3547 (2011)CrossRefGoogle Scholar
  2. 2.
    Jiang, D., Xu, Z., Xu, H.: A novel hybrid prediction algorithm to network traffic. Ann. Telecommun. 70(9), 427–439 (2015)CrossRefGoogle Scholar
  3. 3.
    Soule, A., Lakhina, A., Taft, N., et al.: Traffic matrices: balancing measurements, inference and modeling. In: Proceedings of SIGMETRICS 2005, vol. 33, no. 1, pp. 362–373 (2005)MathSciNetCrossRefGoogle Scholar
  4. 4.
    Takeda, T., Shionoto, K.: Traffic matrix estimation in large-scale IP networks. In: Proceedings of LANMAN 2010, pp. 1–6 (2010)Google Scholar
  5. 5.
    Yingxun, F.: The Research and Improvement of the Genetic Algorithm. Beijing University of Posts and Telecommunications, Beijing (2010)Google Scholar
  6. 6.
    Jiang, D., Zhao, Z., Xu, Z., et al.: How to reconstruct end-to-end traffic based on time-frequency analysis and artificial neural network. AEU-Int. J. Electron. Commun. 68(10), 915–925 (2014)CrossRefGoogle Scholar
  7. 7.
    Jiang, D., Yuan, Z., Zhang, P., et al.: A traffic anomaly detection approach in communication networks for applications of multimedia medical devices. Multimedia Tools Appl. 75, 14281–14301 (2016)CrossRefGoogle Scholar
  8. 8.
    Jiang, D., Xu, Z., Nie, L., et al.: An approximate approach to end-to-end traffic in communication networks. Chin. J. Electron. 21(4), 705–710 (2012)Google Scholar
  9. 9.
    Vaton, S., Bedo, J.: Network traffic matrix: how can one learn the prior distributions from the link counts only. In: Proceedings of ICC 2004, pp. 2138–2142 (2004)Google Scholar
  10. 10.
    Lad, M., Oliveira, R., Massey, D., et al.: Inferring the origin of routing changes using link weights. In: Proceedings of ICNP, pp. 93–102 (2007)Google Scholar
  11. 11.
    Jiang, D., Xu, Z., Li, W., et al.: Topology control-based collaborative multicast routing algorithm with minimum energy consumption. Int. J. Commun Syst 30(1), 1–18 (2017)CrossRefGoogle Scholar
  12. 12.
    Jiang, D., Nie, L., Lv, Z., et al.: Spatio-temporal Kronecker compressive sensing for traffic matrix recovery. IEEE Access 4, 3046–3053 (2016)CrossRefGoogle Scholar
  13. 13.
    Tune, P., Veitch, D.: Sampling vs sketching: an information theoretic comparison. In: Proceedings of INFOCOM, pp. 2105–2113 (2011)Google Scholar
  14. 14.
    Jiang, D., Li, W., Lv, H.: An energy-efficient cooperative multicast routing in multi-hop wireless networks for smart medical applications. Neurocomputing 220(2017), 160–169 (2017)CrossRefGoogle Scholar
  15. 15.
    Zhang, Y., Roughan, M., Duffield, N., et al.: Fast accurate computation of large-scale IP traffic matrices from link loads. In: Proceedings of SIGMETRICS 2003, vol. 31, no. 3, pp. 206–217 (2003)CrossRefGoogle Scholar
  16. 16.
    Jiang, D., Wang, Y., Han, Y., et al.: Maximum connectivity-based channel allocation algorithm in cognitive wireless networks for medical applications. Neurocomputing 2017(220), 41–51 (2017)CrossRefGoogle Scholar
  17. 17.
    Jiang, D., Wang, W., Shi, L., Song, H.: A compressive sensing-based approach to end-to-end network traffic reconstruction. IEEE Trans. Netw. Sci. Eng. (2018).
  18. 18.
    Jiang, D., Huo, L., Song, H.: Rethinking behaviors and activities of base stations in mobile cellular networks based on big data analysis. IEEE Trans. Netw. Sci. Eng. 1(1), 1–12 (2018)CrossRefGoogle Scholar
  19. 19.
    Jiang, D., Huo, L., Li, Y.: Fine-granularity inference and estimations to network traffic for SDN. PLoS ONE 13(5), 1–23 (2018)Google Scholar
  20. 20.
    Jiang, D., Huo, L., Lv, Z., et al.: A joint multi-criteria utility-based network selection approach for vehicle-to-infrastructure networking. IEEE Trans. Intell. Transp. Syst. 99, 1–15 (2018)Google Scholar

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

Personalised recommendations