Analysis of evolutionary process of fog computing system based on BA and ER network hybrid model


Fog computing is oriented to the Internet of Things, which integrates network, computing, storage and application capabilities. It is a semi-virtualized distributed service computing paradigm. It extends data, data processing and applications to the edge of the network and provides intelligent services for users nearby. The purpose of this paper is to design a safe, stable and efficient fog computing model. On the basis of the structure of fog computing system, the evolution process of fog computing nodes is modeled based on BA scale-free network and ER stochastic network model. Then the evolution process of network hybrid model is analyzed. Finally, the evolution model of fog computing system is solved, and a network model with two network characteristics is obtained. Experiments show that the hybrid network model has the advantages of two basic networks.

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

Fig. 1
Fig. 2
Fig. 3


  1. 1.

    Firdhous M, Ghazali O, Hassan S (2014) Fog computing: will it be the future of cloud computing? In: Third international conference on informatics and applications, Kuala Terengganu, Malaysia, Malaysia, pp 8–15

  2. 2.

    Saharan KP, Kumar A (2015) Fog in comparison to cloud: a survey. Int J Comput Appl 122(3):10–12

    Google Scholar 

  3. 3.

    Mahmud R, Buyya R Fog computer: a taxonomy survey and future directions. arXiv preprint arXiv:1611.05539.2016

  4. 4.

    Jalali F, Hinton K, Ayre R et al (2016) Fog computing may help to save energy in cloud computing. IEEE J Sel Areas Commun 34(5):1728–1739

    Article  Google Scholar 

  5. 5.

    Zeng D, Gu L, Guo S et al (2016) Joint optimization of task scheduling and image placement in fog computing supported software-defined embedded system. IEEE Trans Comput 65(12):3702–3712

    MathSciNet  Article  Google Scholar 

  6. 6.

    Dsouza C, Ahn GJ, Taguinod M (2014) Policy-driven security management for fog computing: preliminary framework and a case study. In: 2014 IEEE 15th international conference on information reuse and integration (IRI). IEEE, pp 16–23

  7. 7.

    Al Faruque MA, Vatanparvar K (2016) Energy management-as-a-service over fog computing platform. IEEE Internet Thing J 3(2):161–169

    Article  Google Scholar 

  8. 8.

    Giang NK, Blackstock M, Lea R et al (2015) Developing iot applications in the fog: a distributed dataflow approach. In: 2015 5th international conference on the internet of things (IOT). IEEE, pp 155–162

  9. 9.

    Do CT, Tran NH, Pham C et al (2015) A proximal algorithm for joint resource allocation and minimizing carbon footprint in geo-distributed fog computing. In: 2015 international conference on information networking (ICOIN). IEEE, pp 324–329

  10. 10.

    Mandlekar VG, Mahale VK, Sancheti SS et al (2014) Survey on fog computing mitigating data theft attacks in cloud. Int J Innov Res Comput Sci Technol (IJIRCST) 2(6):13–16

    Google Scholar 

  11. 11.

    Hoang DT, Lu X, Niyato D et al (2015) Applications of repeated games in wireless networks: a survey. IEEE Commun Surv Tutor 17(4):2102–2135

    Article  Google Scholar 

Download references


This paper is supported by Scientific and Technological Project of Henan Province (No. 182102210486), Key Scientific Research Project of University in Henan Province (No. 18A520008).

Author information



Corresponding author

Correspondence to Kunpeng Kang.

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

Verify currency and authenticity via CrossMark

Cite this article

Kang, K. Analysis of evolutionary process of fog computing system based on BA and ER network hybrid model. Evol. Intel. 13, 33–38 (2020).

Download citation


  • Fog computing system
  • BA scale-free network
  • ER random network
  • Evolutionary process