Skip to main content
SpringerLink
Log in

Design of Networking Network Model Based on Network Function Virtualization Technology

  • Conference paper
  • First Online:
The 2020 International Conference on Machine Learning and Big Data Analytics for IoT Security and Privacy (SPIOT 2020)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 1283))

  • 1401 Accesses

Abstract

With the development of society, the living standards of our people have been continuously improved. More and more people are changing from walking to driving. China’s auto sales are also increasing year by year. The purpose of this article is to design a networking model for the Internet of Vehicles based on network function virtualization technology. In terms of method, this paper mainly designs the preliminary structure of the model, and proposes to improve it under the PON technology, and uses the PON system, which is mainly composed of optical line terminal (OLT), optical distribution network (ODN), and optical network unit (ONU) composition. This paper proposes the design of FTTH networking mode based on EPON technology. Since FTTH requires a large number of splitters and PON ports, the splitter method is used in computer rooms or optical intersections. In terms of experiments, the validity of the FTTH networking model proposed in this paper is mainly verified. By comparing the pass rate of the four subjects of the driver’s license with those of the driver’s license, it is found that the pass rate of the vehicle-connected FTTH networking mode is higher than that of the driver’s license, and exceeds 96%.

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

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 169.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 219.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Tang, H., Yuan, Q., Lu, G.: A model for virtualized network function deployment based on node-splitting in vEPC. J. Electron. Inf. Technol. 39(3), 546–553 (2017)

    Google Scholar 

  2. Cotroneo, D., Natella, R., Rosiello, S.: NFV-Throttle: an overload control framework for network function virtualization. IEEE Trans. Netw. Serv. Manage. 14(4), 949–963 (2017)

    Article  Google Scholar 

  3. Sun, C., Bi, J.: HYPER: a hybrid high-performance framework for network function virtualization. IEEE J. Sel. Areas Commun. 35(11), 2490–2500 (2017)

    Article  Google Scholar 

  4. Li, H., Mao, X., Wu, C.: Design and analysis of a general data evaluation system based on social networks. EURASIP J. Wirel. Commun. Netw. 2018(1), 109 (2018)

    Article  Google Scholar 

  5. Pan, C., Zhang, R., Chen, L.: Research on variable current regenerative braking control strategy based on radial basis function neural network tuning PID control. J. Comput. Theor. Nanosci. 14(1), 468–476 (2017)

    Article  Google Scholar 

  6. Li, G., Zhao, G., Zhou, C.: Stochastic elastic properties of composite matrix material with random voids based on radial basis function network. Int. J. Comput. Methods 15(1), 1750082 (2017)

    Article  MathSciNet  Google Scholar 

  7. Long, P.M., Sedghi, H.: On the effect of the activation function on the distribution of hidden nodes in a deep network. Neural Comput. 31(12), 1–19 (2019)

    Article  MathSciNet  Google Scholar 

  8. Wu, W., Liu, M., Yu, S.: Current model analysis of South China sea based on empirical orthogonal function (EOF) decomposition and prototype monitoring data. J. Ocean Univ. China 18(2), 305–316 (2019)

    Article  Google Scholar 

  9. Liu, L., Cheng, X., Lai, J.: Segmentation method for cotton canopy image based on improved fully convolutional network model. Trans. Chin. Soc. Agric. Eng. 34(12), 193–201 (2018)

    Google Scholar 

  10. Liu, J., Duan, S., Li, T.: Design of RBF neural network control system based on spintronic memristor for robotic manipulator. Yi Qi Yi Biao Xue Bao/Chin. J. Sci. Instr. 39(8), 212–219 (2018)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Zhaotong College, Zhaotong, 657000, China

    Jin Bao

Authors
  1. Jin Bao
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Jin Bao .

Editor information

Editors and Affiliations

  1. David Goldman Informatics Centre, University of Sunderland, Sunderland, UK

    John MacIntyre

  2. University of Shanghai for Science and Technology, Shanghai, China

    Jinghua Zhao

  3. Shenzhen University, Shenzen, Guangdong, China

    Xiaomeng Ma

Rights and permissions

Reprints and permissions

Copyright information

© 2021 The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Bao, J. (2021). Design of Networking Network Model Based on Network Function Virtualization Technology. In: MacIntyre, J., Zhao, J., Ma, X. (eds) The 2020 International Conference on Machine Learning and Big Data Analytics for IoT Security and Privacy. SPIOT 2020. Advances in Intelligent Systems and Computing, vol 1283. Springer, Cham. https://doi.org/10.1007/978-3-030-62746-1_16

Download citation

Publish with us

Policies and ethics

Search

Navigation