Deployment of Fog and Edge Computing in IoT for Cyber-Physical Infrastructures in the 5G Era

  • Sultan AhmadEmail author
  • Mohammad Mazhar Afzal
Conference paper
Part of the Lecture Notes on Data Engineering and Communications Technologies book series (LNDECT, volume 39)


To bridge the gap between existing cloud-based ICT architectures and large-scale Internet of Things (IoT) deployment, fog and edge computing (FEC) is becoming promising and anticipating paradigm to facilitate computing and communication for the next generation cyber-physical infrastructures. The key features of fog and edge computing include low latency, locality, scalability, security and privacy. Those features form a solid foundation to meet the service requirements of smart cyber-physical systems. However, although there are stand-alone, solutions exist already in this domain; many aspects are still unexplored yet in practical context also. In addition, we lack a coherent edge computing architecture that embeds these principles and can effectively enhance our cyber-physical infrastructures. In this research paper, we investigate how to enhance many expected features such as self-adaptiveness and resilience in cyber-physical systems in 5G era and IoT deployment. The focus is on fog and edge computing, which encompasses computing, communication, data analytics, security and privacy.


Fog Computing Cloud Computing Edge Computing Security and privacy IoT Cyber-physical infrastructure 


  1. 1.
    Ahn, G., Park, Y.-J., Hur, S.: Probabilistic graphical framework for estimating collaboration levels in cloud manufacturing. Sustainability 9, 277 (2017)CrossRefGoogle Scholar
  2. 2.
    Ostberg, P.O., Byrne, J., Casari, P., Eardley, P., Fernandez Anta, A., Forsman, J., Kennedy, J., Duc, T.L., Mariño, M.N., Loomba, R., et al.: Reliable capacity provisioning for distributed cloud/edge/fog computing applications. In: Proceedings of the 2017 European Conference on Networks and Communications (EuCNC), Oulu, Finland, 12–15 June 2017, pp. 1–6 (2017)Google Scholar
  3. 3.
    Xu, J., Palanisamy, B., Ludwig, H., Wang, Q.: Zenith: utility-aware resource allocation for edge computing. In: Proceedings of the IEEE International Conference on Edge Computing (EDGE), Honolulu, HI, USA, 25–30 June 2017, pp. 47–54 (2017)Google Scholar
  4. 4.
    Wang, N., Varghese, B., Matthaiou M., Nikolopoulos, D.S.: ENORM: a framework for edge node resource management. IEEE Trans. Serv. Comput. (2017)Google Scholar
  5. 5.
    Liu, H., Eldarrat, F., Alqahtani, H., Reznik, A., de Foy, X., Zhang, Y.: Mobile edge cloud system: architectures, challenges, and approaches. IEEE Syst. J. 12, 1–14 (2017)Google Scholar
  6. 6.
    Garcia-Valls, M., Bellavista, P., Gokhale, A.: Reliable software technologies and communication middleware: A perspective and evolution directions for cyber-physical systems, mobility, and cloud computing. Future Gener. Comput. Syst. 71 (2017)CrossRefGoogle Scholar
  7. 7.
    Elshenawy, M., Abdulhai, B., El-Darieby, M.: Towards a service-oriented cyber–physical systems of systems for smart city mobility applications. Future Gener. Comput. Syst. 79, 575–587 (2018). Part 2. ISSN 0167-739XCrossRefGoogle Scholar
  8. 8.
    Rahmani, A.M., Gia, T.N., Negash, B., Anzanpour, A., Azimi, I., Jiang, M., Liljeberg, P.: Exploiting smart e-Health gateways at the edge of healthcare internet-of-things: a fog computing approach. Future Gener. Comput. Syst. 78, 641–658 (2018). Part 2. ISSN 0167-739XCrossRefGoogle Scholar
  9. 9.
    Firouzi, F., Rahmani, A.M., Mankodiya, K., Badaroglu, M., Merrett, G.V., Wong, P., Farahani, B.: Internet-of-things and big data for smarter healthcare: from device to architecture, applications and analytics. Future Gener. Comput. Syst. 78, 583–586 (2018). Part 2. ISSN 0167-739XCrossRefGoogle Scholar
  10. 10.
    Cicirelli, F., Guerrieri, A., Spezzano, G., Vinci, A.: An edge-based platform for dynamic Smart City applications. Future Gener. Comput. Syst. 76, 106–118 (2017). ISSN 0167-739XCrossRefGoogle Scholar
  11. 11.
    Ghosh, R., Simmhan, Y.: Distributed scheduling of event analytics across edge and cloud. ACM Trans. Cyber-Phys. Syst. 2(4), 28 (2018). Article 24CrossRefGoogle Scholar
  12. 12.
    Psaras, I.: Decentralised edge-omputing and IoT through distributed trust. In: MobiSys 2018: The 16th Annual International Conference on Mobile Systems, Applications, and Services, 10–15 June 2018, Munich, Germany, p. 3. ACM, NewYork (2018).
  13. 13.
    Gupta, H., Vahid Dastjerdi, A., Ghosh, S.K., Buyya, R.: iFogSim: a to olkit for modeling andsimulation of resource management techniques in the internet of things, edge and fog computing environments. Softw. Pract. Exp. 47, 1275–1296 (2017)CrossRefGoogle Scholar
  14. 14.
    Vaquero, M., Rodero-Merino, L.: Finding your way in the fog: towards a comprehensive definition of fog computing ACM SIGCOMM. Comput. Commun. Rev. 44, 27–32 (2014)CrossRefGoogle Scholar
  15. 15.
    Aazam, M., Huh, E.N.: Fog computing: the cloud-IOT/IoE middleware paradigm. IEEE Potentials 35(3), 40–44 (2016)CrossRefGoogle Scholar
  16. 16.
    Hu, P., Dhelim, S., Ning, H., Qiu, T.: Survey on fog computing: architecture, key technologies, applications and open issues. J. Netw. Comput. Appl. 98, 27–42 (2017)CrossRefGoogle Scholar
  17. 17.
    Zamani, A.S., Akhtar, M.M., Ahmad, S.: Emerging cloud computing paradigm. Int. J. Comput. Sci. 8(4), 304 (2011). paper id ‘IJCSI-2011-8-4-164Google Scholar
  18. 18.
    Gartner: Gartner says 6.4 billions connected things will be in USA in 2016.
  19. 19.
    Klonoff, D.C.: Fog computing and edge computing architectures for processing data from diabetes devices connected to the medical Internet of Things. J. Diab. Sci. Technol. 11(4), 647–652 (2017). Scholar

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© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.Department of Computer ScienceGlocal UniversitySaharanpurIndia

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