IoT Cloud Computing, Storage, and Data Analytics

  • Khaled Salah Mohamed


In this chapter, we will discuss the basic motivation behind cloud computing and its importance for IoT. Thanks to TCP/IP and HTTP, any client or IoT hardware can talk to any IoT service, no matter which hardware you choose. We will learn how to use the cloud as an intelligent system.


  1. 1.
    What is an IoT application platform? | Zatar., 2017. [Online]. Retrieved August 13, 2017, from
  2. 2.
    Botta, A., De Donato, W., Persico, V., & Pescapé, A. (2016). Integration of cloud computing and internet of things: A survey. Future Generation Computer Systems, 56, 684.CrossRefGoogle Scholar
  3. 3.
    Retrieved from
  4. 4.
    Mouradian, C., Naboulsi, D., Yangui, S., Glitho, R. H., Morrow, M. J., & Polakos, P. A. (2018). A comprehensive survey on fog Computing: State-ofthe- art and research challenges. IEEE Communications Surveys and Tutorials, 20(1), 416–464.CrossRefGoogle Scholar
  5. 5.
  6. 6.
  7. 7.
    Retrieved from
  8. 8.
  9. 9.
    Retrieved from
  10. 10.
  11. 11.
  12. 12.
    Retrieved from
  13. 13.
    Retrieved from
  14. 14.
    Retrieved from
  15. 15.
    Retrieved from
  16. 16.
  17. 17.
    Ray, P. P. (2016). A survey of IoT cloud platforms. Future Computing and Informatics Journal, 1, 35e46.CrossRefGoogle Scholar
  18. 18.
    Webcast: IoT use-cases with IBM Watson IOT Platform. [Online]. Retrieved September 10, 2017, from IBM Watson IoT Architecture.
  19. 19.
    Aazam, M., Khan, I., Alsaffar, A.A., Huh, E.N. (2014). Cloud of things: Integrating internet of things and cloud Computing and the issues involved. International Bhurban Conference on Applied Sciences and Technology. IEEE.Google Scholar
  20. 20.
    George, G., Haas, M. R., & Pentland, A. (2014). Big data and management. Academy of Management Journal, 57(2), 321–326.CrossRefGoogle Scholar
  21. 21.
    Pan, J., & McElhannon, J. (Feb. 2018). Future edge cloud and edge Computing for internet of things applications. IEEE Internet of Things Journal, 5(1), 439–449.CrossRefGoogle Scholar
  22. 22.
    Bilal, K., Khalid, O., Erbad, A., & Khan, S. U. (Jan. 2018). Potentials, trends, and prospects in edge technologies: Fog, cloudlet, Mobile edge, and micro data centers. Computer Networks, 130, 94–120.CrossRefGoogle Scholar
  23. 23.
    Atlam, H. F., Walters, R. J., & Wills, G. B. (2018). Fog Computing and the internet of things: A review. Big Data and Cognitive Computing, 2(10), 1–18.Google Scholar
  24. 24.
    Botta, A., De Donato, W., Persico, V., et al. (2016). Integration of cloud computing and internet of things[J]. Future Generation Computer Systems, 56(C), 684–700.CrossRefGoogle Scholar
  25. 25.
    Chiang, M., & Zhang, T. (2016). Fog and IoT: An overview of research opportunities[J]. IEEE Internet of Things Journal, 3, 854–864.CrossRefGoogle Scholar
  26. 26.
    Mahmud, R., Buyya, R. (2018). Fog Computing: A taxonomy, survey and future directions. Internet of Everything (pp. 103–130), Springer.Google Scholar
  27. 27.
    Oma, R., Nakamura, S., Duolikun, D., Enokido, T., Takizawa, M. (2018). An energy-efficient model for fog computing in the internet of things (IoT). Internet of Things, 1-2, 14. Elsevier.Google Scholar
  28. 28.
    Leandro Andrade. SOFT-IoT Platform in Fog of Things. WebMedia ‘18, October 16–19, 2018, Salvador-BA, Brazil.Google Scholar
  29. 29.
    Mohamed, K. S. (2018). Machine learning for model order reduction. Springer.Google Scholar
  30. 30.
  31. 31.
    Madden, S. (2012). From databases to big data. IEEE Internet Computing, 16(2012), 4–6.CrossRefGoogle Scholar
  32. 32.
    Agarwal, R., & Dhar, V. (2014). Editorial—Big data, data science, and analytics: The opportunity and challenge for IS research. Information Systems Research, 25(3), 443–448.CrossRefGoogle Scholar
  33. 33.
    Lim, E. P., Chen, H., & Chen, G. (2013). Business intelligence and analytics: Research directions. ACM Transactions on Management Information Systems (TMIS), 3(4), 17.Google Scholar
  34. 34.
    Dhar, V. (2013). Data science and prediction. Communications of the ACM, 56(12), 64–73.CrossRefGoogle Scholar
  35. 35.
    Raghupathi, W., & Raghupathi, V. (2014). Big data analytics in healthcare: Promise and potential. Health Information Science and Systems, 2(1), 3.CrossRefGoogle Scholar
  36. 36.
    Khan, Z., Anjum, A., Soomro, K., & Muhammad, T. (2015). Towards cloud based big data analytics for smart future cities. Journal of Cloud Computing: Advances, Systems and Applications, 4.Google Scholar
  37. 37.
    McAfee, A., & Brynjolfsson, E. (2012). Big data: The management revolution. Harvard business review.Google Scholar
  38. 38.
    Davenport, T. H., & Patil, D. J. (2012). Data scientist. Harvard business review.Google Scholar
  39. 39.
    Provost, F., & Fawcett, T. (2013). Data science and its relationship to big data and data-driven decision making. Big Data, 1(1), 51–59.CrossRefGoogle Scholar
  40. 40.
    Yeoh, W., & Koronios, A. (2010). Critical success factors for business intelligence systems. Journal of Computer Information Systems, 50(3), 23.Google Scholar
  41. 41.
    Davenport, T. H. (2012). Enterprise analytics: Optimize performance, process, and decisions through big data. FT Press.Google Scholar
  42. 42.
    Minelli, M., Chambers, M., & Dhiraj, A. (2013). Big Data, Big Analytics: Emerging business intelligence and analytic trends for Today’s businesses. Hoboken, NJ: Wiley.CrossRefGoogle Scholar
  43. 43.
    Mayer-Schonberger, V., & Cukier, K. (2013). Big Data: A revolution that will transform how we live, work, and think. Eamon Dolan/Houghton Mifflin Harcourt.Google Scholar
  44. 44.
    Dean, J. (2014). Big data, data mining, and machine learning: Value creation for business leaders and practitioners. Hoboken, NJ: Wiley.CrossRefGoogle Scholar
  45. 45.
    Agrawal, D.; Bernstein, P.; Bertino, E.; Davidson, S.; Dayal, U.; Franklin, M.; Widom, J. (2012). Challenges and opportunities with big data: A white paper prepared for the Computing community consortium committee of the Computing Research Association. Retrieved November 13, 2018, from
  46. 46.
  47. 47.
  48. 48.
  49. 49.
  50. 50.
  51. 51.

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Khaled Salah Mohamed
    • 1
  1. 1.Mentor, A Siemens BusinessCairoEgypt

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