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Fog Computing: Data Analytics for Time-Sensitive Applications

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Convergence of Artificial Intelligence and the Internet of Things

Part of the book series: Internet of Things ((ITTCC))

Abstract

Fog computing has been initiated to reduce communications delays between users and cloud systems. The idea of Fog computing allows users to interact with intermediate servers, while reaping the benefits of reliability and elasticity, which are inherent in cloud computing. Fog computing can leverage Internet of Things (IoT) by providing a reliable service layer for time-sensitive applications and real-time analytics. While the concept of fog computing is still evolving, it is pertinent to study the domain of fog computing and analyze its strengths and weaknesses. Motivated by this need, this chapter describes the architecture of fog computing and explain its efficacy with respect to different applications. The chapter highlights some of the key challenges associated with this evolving platform along with future directions of research.

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References

  1. Aazam, M., Huh, E.N.: Fog computing and smart gateway based communication for cloud of things. In: 2014 International Conference on Future Internet of Things and Cloud (FiCloud), pp. 464–470. IEEE (2014)

    Google Scholar 

  2. Aazam, M., Zeadally, S., Harras, K.A.: Offloading in fog computing for IoT: review, enabling technologies, and research opportunities. Future Gener. Comput. Syst. (2018)

    Google Scholar 

  3. Agarwal, S., Yadav, S., Yadav, A.K.: An efficient architecture and algorithm for resource provisioning in fog computing. Int. J. Inf. Eng. Electron. Bus. 8(1), 48 (2016)

    Google Scholar 

  4. Alhaija, H.A., Mustikovela, S.K., Mescheder, L., Geiger, A., Rother, C.: Augmented reality meets deep learning for car instance segmentation in urban scenes. In: British Machine Vision Conference, vol. 1, p. 2 (2017)

    Google Scholar 

  5. Ali, M.: Green cloud on the horizon. In: IEEE International Conference on Cloud Computing, pp. 451–459. Springer (2009)

    Google Scholar 

  6. Andriopoulou, F., Dagiuklas, T., Orphanoudakis, T.: Integrating IoT and fog computing for healthcare service delivery. In: Components and Services for IoT Platforms, pp. 213–232. Springer (2017)

    Google Scholar 

  7. Balevi, E., Gitlin, R.D.: Unsupervised machine learning in 5g networks for low latency communications. In: 2017 IEEE 36th International Performance Computing and Communications Conference (IPCCC), pp. 1–2. IEEE (2017)

    Google Scholar 

  8. Bonomi, F., Milito, R., Natarajan, P., Zhu, J.: Fog computing: a platform for internet of things and analytics. In: Big Data and Internet of Things: A Roadmap for Smart Environments, pp. 169–186. Springer (2014)

    Google Scholar 

  9. Chen, N., Chen, Y., You, Y., Ling, H., Liang, P., Zimmermann, R.: Dynamic urban surveillance video stream processing using fog computing. In: 2016 IEEE Second International Conference on Multimedia Big Data (BigMM), pp. 105–112. IEEE (2016)

    Google Scholar 

  10. Chen, Y., Abraham, A., Yang, B.: Hybrid flexible neural-tree-based intrusion detection systems. Int. J. Intell. Syst. 22(4), 337–352 (2007)

    Google Scholar 

  11. Chiang, M., Ha, S., Chih-Lin, I., Risso, F., Zhang, T.: Clarifying fog computing and networking: 10 questions and answers. IEEE Commun. Mag. 55(4), 18–20 (2017)

    Google Scholar 

  12. Chiang, M., Zhang, T.: Fog and IoT: an overview of research opportunities. IEEE Internet Things J. 3(6), 854–864 (2016)

    Google Scholar 

  13. Cho, S.-B.: Exploiting machine learning techniques for location recognition and prediction with smartphone logs. Neurocomputing 176, 98–106 (2016)

    Article  Google Scholar 

  14. Dastjerdi, A.V., Gupta, H., Calheiros, R.N., Ghosh, S.K., Buyya, R.: Fog computing: principles, architectures, and applications. In: Internet of Things, pp. 61–75. Elsevier (2016)

    Google Scholar 

  15. Diro, A.A., Chilamkurti, N.: Distributed attack detection scheme using deep learning approach for internet of things. Future Gener. Comput. Syst. 82, 761–768 (2018)

    Google Scholar 

  16. Dsouza, C., Ahn, G.J., Taguinod, M.: 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), pp 16–23. IEEE (2014)

    Google Scholar 

  17. Hoque, S., de Brito, M.S., Willner, A., Keil, O., Magedanz, T.: Towards container orchestration in fog computing infrastructures. In: 2017 IEEE 41st Annual Computer Software and Applications Conference (COMPSAC), vol. 2, pp. 294–299. IEEE (2017)

    Google Scholar 

  18. Kaur, K., Dhand, T., Kumar, N., Zeadally, S.: Container-as-a-service at the edge: trade-off between energy efficiency and service availability at fog nano data centers. IEEE Wirel. Commun. 24(3), 48–56 (2017)

    Google Scholar 

  19. Lee, K., Kim, D., Ha, D., Rajput, U., Oh, H.: On security and privacy issues of fog computing supported internet of things environment. In: 2015 6th International Conference on the Network of the Future (NOF), pp. 1–3. IEEE (2015)

    Google Scholar 

  20. Luan, T.H., Gao, L., Li, Z., Xiang, Y., Wei, G., Sun, L.: Fog computing: focusing on mobile users at the edge. arXiv preprint arXiv:1502.01815 (2015)

  21. MacArthur, P., Liu, Q., Russell, R.D., Mizero, F., Veeraraghavan, M., Dennis, J.M.: An integrated tutorial on infiniband, verbs, and MPI. IEEE Commun. Surv. Tutor. 19(4), 2894–2926 (2017)

    Google Scholar 

  22. Mahmud, R., Kotagiri, R., Buyya, R.: Fog computing: a taxonomy, survey and future directions. In: Internet of Everything, pp. 103–130. Springer (2018)

    Google Scholar 

  23. Markakis, E., Mastorakis, G., Mavromoustakis, C.X., Pallis, E.: Cloud and Fog Computing in 5G Mobile Networks: Emerging Advances and Applications. Institution of Engineering and Technology (2017)

    Google Scholar 

  24. Markakis, E.K., Karras, K., Zotos, N., Sideris, A., Moysiadis, T., Corsaro, A., Alexiou, G., Skianis, C., Mastorakis, G., Mavromoustakis, C.X., et al.: Exegesis: extreme edge resource harvesting for a virtualized fog environment. IEEE Commun. Mag. 55(7), 173–179 (2017)

    Google Scholar 

  25. Nikoloudakis, Y., Panagiotakis, S., Markakis, E., Pallis, E., Mastorakis, G., Mavromoustakis, C.X., Dobre, C.: A fog-based emergency system for smart enhanced living environments. IEEE Cloud Comput. (6), 54–62 (2016)

    Google Scholar 

  26. Pauly, O., Diotte, B., Fallavollita, P., Weidert, S., Euler, E., Navab, N.: Machine learning-based augmented reality for improved surgical scene understanding. Comput. Med. Imag. Graph. 41, 55–60 (2015)

    Article  Google Scholar 

  27. Perera, C., Qin, Y., Estrella, J.C., Reiff-Marganiec, S., Vasilakos, A.V.: Fog computing for sustainable smart cities: a survey. ACM Comput. Surv. (CSUR) 50(3), 32 (2017)

    Google Scholar 

  28. Pham, X.Q., Huh, E.N.: Towards task scheduling in a cloud-fog computing system. In: 2016 18th Asia-Pacific Network Operations and Management Symposium (APNOMS), pp. 1–4. IEEE (2016)

    Google Scholar 

  29. Roman, R., Lopez, J., Mambo, M.: Mobile edge computing, fog et al.: A survey and analysis of security threats and challenges. Future Gener. Comput. Syst. 78, 680–698 (2018)

    Google Scholar 

  30. Salonikias, S., Mavridis, I., Gritzalis, D.: Access control issues in utilizing fog computing for transport infrastructure. In: International Conference on Critical Information Infrastructures Security, pp. 15–26. Springer (2015)

    Google Scholar 

  31. Sheikh, F., Fazal, H., Taqvi, F., Shamsi, J.: Power-aware server selection in nano data center. In: 2015 IEEE 40th Local Computer Networks Conference Workshops (LCN Workshops), pp. 776–782. IEEE (2015)

    Google Scholar 

  32. Shi, W., Cao, J., Zhang, Q., Li, Y., Lanyu, X.: Edge computing: vision and challenges. IEEE Internet Things J. 3(5), 637–646 (2016)

    Article  Google Scholar 

  33. Tang, B., Chen, Z., Hefferman, G., Pei, S., Wei, T., He, H., Yang, Q.: Incorporating intelligence in fog computing for big data analysis in smart cities. IEEE Trans. Ind. Inf. 13(5), 2140–2150 (2017)

    Article  Google Scholar 

  34. Tang, B., Chen, Z., Hefferman, G., Wei, T., He, H., Yang, Q.: A hierarchical distributed fog computing architecture for big data analysis in smart cities. In: Proceedings of the ASE BigData & SocialInformatics, p. 28. ACM (2015)

    Google Scholar 

  35. Teerapittayanon, S., McDanel, B., Kung, H.T.: Distributed deep neural networks over the cloud, the edge and end devices. In: 2017 IEEE 37th International Conference on Distributed Computing Systems (ICDCS, pp. 328–339. IEEE (2017)

    Google Scholar 

  36. Vaquero, L.M., Rodero-Merino, L.: Finding your way in the fog: towards a comprehensive definition of fog computing. ACM SIGCOMM Comput. Commun. Rev. 44(5), 27–32 (2014)

    Google Scholar 

  37. Varghese, B., Wang, N., Nikolopoulos, D.S., Buyya, R.: Feasibility of fog computing. arXiv preprint arXiv:1701.05451 (2017)

  38. Williams, J.B.: Fibre channel over ethernet, 8 July 2014. US Patent 8,774,215 (2014)

    Google Scholar 

  39. Yi, S., Li, C., Li, Q.: A survey of fog computing: concepts, applications and issues. In: Proceedings of the 2015 Workshop on Mobile Big Data, pp. 37–42. ACM (2015)

    Google Scholar 

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Author information

Authors and Affiliations

  1. National University of Computer and Emerging Sciences, Karachi, Pakistan

    Jawwad A. Shamsi & Muhammad Hanif

  2. University of Kentucky, Lexington, USA

    Sherali Zeadally

Authors
  1. Jawwad A. Shamsi
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  2. Muhammad Hanif
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  3. Sherali Zeadally
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Corresponding author

Correspondence to Jawwad A. Shamsi .

Editor information

Editors and Affiliations

  1. Department of Management Science and Technology, Hellenic Mediterranean University, Agios Nikolaos, Crete, Greece

    George Mastorakis

  2. Mobile Systems Laboratory (MoSys Lab), Department of Computer Science, Research Centres (UNRF) – University of Nicosia, Nicosia, Cyprus

    Constandinos X. Mavromoustakis

  3. Department of Telecommunications, Warsaw University of Technology, Warsaw, Poland

    Jordi Mongay Batalla

  4. Department of Electrical and Computer Engineering, Hellenic Mediterranean University, Heraklion, Crete, Greece

    Evangelos Pallis

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Shamsi, J.A., Hanif, M., Zeadally, S. (2020). Fog Computing: Data Analytics for Time-Sensitive Applications. In: Mastorakis, G., Mavromoustakis, C., Batalla, J., Pallis, E. (eds) Convergence of Artificial Intelligence and the Internet of Things. Internet of Things. Springer, Cham. https://doi.org/10.1007/978-3-030-44907-0_1

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  • DOI: https://doi.org/10.1007/978-3-030-44907-0_1

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