Skip to main content
SpringerLink
Log in

Jitter Buffer Control Algorithm and Simulation Based on Network Traffic Prediction

  • Published:
International Journal of Wireless Information Networks Aims and scope Submit manuscript

Abstract

The jitter buffer size is one of the important factors that affect the voice quality of Voice over Internet Protocol (VoIP). In order to reduce buffer delay and packet loss and improve the speech quality of VoIP, an adaptive jitter buffer control algorithm based on network traffic prediction is proposed. Among them, the particle swarm optimization algorithm is used to optimize the weight and threshold of Elman neural network so that it avoids falling into local minimum and improves the accuracy of network traffic prediction. According to the change law of network traffic, the jitter buffer control algorithm under the autoregressive model is proposed. Through the improved stochastic midpoint placement algorithm, a network business traffic prediction model with sudden and self-similarity is established. Then the buffer size is set according to the predicted value of network traffic, and it is continuously improved in use to improve the accuracy of the buffer setting. The simulation results show that the MOS value of the algorithm is high, which improves the voice quality of the network telephone.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11

References

  1. U. Shaw and B. Sharma, A survey paper on voice over internet protocol (VOIP), International Journal of Computer Applications, Vol. 139, No. 2, pp. 16–22, 2016.

    Article  Google Scholar 

  2. N. Hynes and A. D. Elwell, The role of inter-organizational networks in enabling or delaying disruptive innovation: a case study of mVoIP, Journal of Business and Industrial Marketing, Vol. 31, No. 6, pp. 722–731, 2016.

    Article  Google Scholar 

  3. M. Ruta, F. Scioscia, F. Gramegna, et al., A knowledge fusion approach for context awareness in vehicular networks, IEEE Internet of Things Journal, Vol. 5, No. 4, pp. 2407–2419, 2018.

    Article  Google Scholar 

  4. X. Ma, Z. Dai, Z. He, et al., Learning traffic as images: a deep convolutional neural network for large-scale transportation network speed prediction, Sensors, Vol. 17, No. 4, p. 818, 2017.

    Article  Google Scholar 

  5. N. G. Polson and V. O. Sokolov, Deep learning for short-term traffic flow prediction, Transportation Research Part C: Emerging Technologies, Vol. 79, No. 1, pp. 1–17, 2017.

    Article  Google Scholar 

  6. Y. T. Chae, R. Horesh, Y. Hwang, et al., Artificial neural network model for forecasting sub-hourly electricity usage in commercial buildings, Energy and Buildings, Vol. 111, No. 1, pp. 184–194, 2016.

    Article  Google Scholar 

  7. J. Li, X. Mei, D. Prokhorov, et al., Deep neural network for structural prediction and lane detection in traffic scene, IEEE Transactions on Neural Networks and Learning Systems, Vol. 28, No. 3, pp. 690–703, 2017.

    Article  Google Scholar 

  8. M. E. Jalal, M. Hosseini and S. Karlsson, Forecasting incoming call volumes in call centers with recurrent neural networks, Journal of Business Research, Vol. 69, No. 11, pp. 4811–4814, 2016.

    Article  Google Scholar 

  9. A. H. Hasan and W. H. Laith, Online Elman neural network training by genetic algorithm, Journal of Advances in Mathematics and Computer Science, Vol. 4, pp. 1–15, 2016.

    Google Scholar 

  10. H. G. Han, Y. N. Guo and J. F. Qiao, Nonlinear system modeling using a self-organizing recurrent radial basis function neural network, Applied Soft Computing, Vol. 71, pp. 1105–1116, 2018.

    Article  Google Scholar 

  11. R. Kumar, S. Srivastava, J. R. P. Gupta, et al., Diagonal recurrent neural network based identification of nonlinear dynamical systems with Lyapunov stability based adaptive learning rates, Neurocomputing, Vol. 287, pp. 102–117, 2018.

    Article  Google Scholar 

  12. M. Jahangir, M. Golshan, S. Khosravi, et al., Design of a fast convergent backpropagation algorithm based on optimal control theory, Nonlinear Dynamics, Vol. 70, No. 2, pp. 1051–1059, 2012.

    Article  MathSciNet  Google Scholar 

  13. J. D. Rios, A. Y. Alanis, C. Lopez-Franco, et al., RHONN identifier-control scheme for nonlinear discrete-time systems with unknown time-delays, Journal of the Franklin Institute, Vol. 355, No. 1, pp. 218–249, 2018.

    Article  MathSciNet  MATH  Google Scholar 

  14. R. Kumar, S. Srivastava and J. R. P. Gupta, Lyapunov stability-based control and identification of nonlinear dynamical systems using adaptive dynamic programming, Soft Computing, Vol. 21, No. 15, pp. 4465–4480, 2017.

    Article  MATH  Google Scholar 

  15. Y. J. Liang, N. Farber and B. Girod, Adaptive playout scheduling and loss concealment for voice communication over IP networks, IEEE Transactions on Multimedia, Vol. 5, No. 4, pp. 532–543, 2003.

    Article  Google Scholar 

  16. M. Narbutt, A. Kelly, P. Perry, et al., Adaptive VoIP playout scheduling: assessing user satisfaction, IEEE Internet Computing, Vol. 9, No. 4, pp. 28–34, 2005.

    Article  Google Scholar 

  17. M. Seufert, S. Egger, M. Slanina, et al., A survey on quality of experience of HTTP adaptive streaming, IEEE Communications Surveys and Tutorials, Vol. 17, No. 1, pp. 469–492, 2015.

    Article  Google Scholar 

  18. Q. Cui, Y. Wang, K. C. Chen, et al., Big data analytics and network calculus enabling intelligent management of autonomous vehicles in a smart city. IEEE Internet of Things Journal, Vol. 6, No. 2, pp. 2021–2034, 2018.

    Article  Google Scholar 

  19. H. Hadama, M. Ogura, and R. Nakamura. Effectiveness of a fast switching function of continuous video signals on unmanned vehicle controls. Memoirs of the National Defense Academy, Vol. 57, No. 1, pp. 11–21, 2017.

    Google Scholar 

  20. H. G. Kim and J. H. Lee, Enhancing VoIP speech quality using combined playout control and signal reconstruction, IEEE Transactions on consumer Electronics, Vol. 58, No. 2, pp. 562–569, 2012.

    Article  Google Scholar 

  21. A. Lykourgiotis, S. Kotsopoulos and T. Dagiuklas, A novel mobility-aware playout algorithm for VoIP services, Wireless Personal Communications, Vol. 96, No. 2, pp. 2427–2446, 2017.

    Article  Google Scholar 

  22. F. Sakuray, R. S. V. Hoto and L. S. Mendes, Analysis and estimation of playout delay in VoIP communications, International Journal of Computer Science and Network Security, Vol. 8, No. 3, pp. 98–105, 2008.

    Google Scholar 

  23. L. Bhebhe and R. Parkkali, VoIP performance over HSPA with different VoIP clients, Wireless Personal Communications, Vol. 58, No. 3, pp. 613–626, 2011.

    Article  Google Scholar 

Download references

Funding

Scientific and Technological Research Project of Chongqing Education Commission: “Design of User Experience Experiment System for Smart Home” (KJQN201805301).

Author information

Authors and Affiliations

  1. Department of Commerce and Trade Management, Chongqing Business Vocational College, Chongqing, 401520, China

    Minglan Yuan

  2. Department of Automobile and Electronic Engineering, Chongqing Vocational College of Applied Technology, Chongqing, 401520, China

    Minglan Yuan

Authors
  1. Minglan Yuan
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Minglan Yuan.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Yuan, M. Jitter Buffer Control Algorithm and Simulation Based on Network Traffic Prediction. Int J Wireless Inf Networks 26, 133–142 (2019). https://doi.org/10.1007/s10776-019-00429-8

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10776-019-00429-8

Keywords

Search

Navigation