On Internet of Things Programming Models

Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 678)

Abstract

In this paper, we present the review of existing and proposed programming models for Internet of Things (IoT) applications. The requests by the economy and the development of computer technologies (e.g., cloud-based models) have led to an increase in large-scale projects in the IoT area. The large-scale IoT systems should be able to integrate diverse types of IoT devices and support big data analytics. And, of course, they should be developed and updated at a reasonable cost and within a reasonable time. Due to the complexity, scale, and diversity of IoT systems, programming for IoT applications is a great challenge. And this challenge requires programming models and development systems at all stages of development and for all aspects of IoT development. The first target for this review is a set of existing and future educational programs in information and communication technologies at universities, which, obviously, must somehow respond to the demands of the development of IoT systems.

Keywords

Internet of Things Smart Cities Streaming Sensor fusion Programming Education 

References

  1. 1.
    Chen, Y.-K.: Challenges and opportunities of internet of things. In: Design Automation Conference (ASP-DAC), pp. 383–388. IEEE Press, New York (2012)Google Scholar
  2. 2.
    Namiot, D., Sneps-Sneppe, M.: On IoT programming. Int. J. Open Inf. Technol. 2(10), 25–28 (2014)Google Scholar
  3. 3.
    Namiot, D., Sneps-Sneppe, M.: On software standards for smart cities: API or DPI. In: ITU Kaleidoscope Academic Conference: Living in a Converged World-Impossible Without Standards? pp. 169–174. IEEE Press, New York (2014)Google Scholar
  4. 4.
    Im, J., Seonghoon, K., Daeyoung, K.: IoT mashup as a service: cloud-based mashup service for the internet of things. In: 2013 IEEE International Conference on Services Computing (SCC), pp. 462–469. IEEE Press, New York (2013)Google Scholar
  5. 5.
    Namiot, D., Sneps-Sneppe, M.: On micro-services architecture. Int. J. Open Inf. Technol. 2(9), 24–27 (2014)Google Scholar
  6. 6.
    Bahrami, M., Singhal, M.: The role of cloud computing architecture in big data. In: Pedrycz, W., Chen, S.-M. (eds.) Information Granularity, Big Data, and Computational Intelligence, pp. 275–295. Springer, Heidelberg (2015)Google Scholar
  7. 7.
    Raggett, D.: The internet of things: W3C plans for developing standards for open markets of services for the IoT. Ubiquity 10, 3–6 (2015)Google Scholar
  8. 8.
  9. 9.
  10. 10.
    Aggarwal, C.C.: Managing and Mining Sensor Data. Springer Science & Business Media, New York (2013)CrossRefGoogle Scholar
  11. 11.
    Wang, M., et al.: City data fusion: sensor data fusion in the internet of things. arXiv preprint arXiv:1506.09118 (2015)
  12. 12.
    Introduction to sensor fusion. http://projects.mbientlab.com/introduction-to-sensor-fusion/. Accessed May 2016
  13. 13.
    Marz, N., Warren, J.: Big Data: Principles and Best Practices of Scalable Realtime Data Systems. Manning Publications Co., Greenwich (2015)Google Scholar
  14. 14.
    Ranjan, R.: Streaming big data processing in datacenter clouds. IEEE Cloud Comput. 1, 78–83 (2014)CrossRefGoogle Scholar
  15. 15.
    Applying the Kappa architecture in the telco industry. https://www.oreilly.com/ideas/applying-the-kappa-architecture-in-the-telco-industry. Accessed May 2016
  16. 16.
    Villari, M., et al.: AllJoyn Lambda: an architecture for the management of smart environments in IoT. In: Smart Computing Workshops (SMARTCOMP Workshops), pp. 9–14. IEEE Press, New York (2014)Google Scholar
  17. 17.
    Erb, B., Kargl, F.: A conceptual model for event-sourced graph computing. In: Proceedings of the 9th ACM International Conference on Distributed Event-Based Systems, pp. 352–355. ACM, New York (2015)Google Scholar
  18. 18.
    Garg, N.: Apache Kafka. Packt Publishing Ltd., Birmingham (2013)Google Scholar
  19. 19.
    Shanahan, J.G., Laing, D.: Large scale distributed data science using apache spark. In: Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 2323–2324. ACM, New York (2015)Google Scholar
  20. 20.
  21. 21.
    Namiot, D.: On internet of things and smart cities educational courses. Int. J. Open Inf. Technol. 4(5), 26–38 (2016)Google Scholar
  22. 22.
  23. 23.
    OpenStack. https://www.openstack.org/. Accessed Aug 2016
  24. 24.
    Jackson, K., Bunch, C., Sigler, E.: OpenStack Cloud Computing Cookbook. Packt Publishing Ltd., Birmingham (2015)Google Scholar
  25. 25.
    Sneps-Sneppe, M., Namiot, D.: On mobile cloud for smart city applications. arXiv preprint arXiv:1605.02886 (2016)
  26. 26.
    Bonomi, F., Milito, R., Zhu, J., Addepalli, S.: Fog computing and its role in the internet of things. In: Proceedings of the 1st edn. of the MCC workshop on Mobile Cloud Computing, pp. 13–16. ACM, New York (2012)Google Scholar
  27. 27.
    Byers, C.C., Wetterwald, P.: Fog computing distributing data and intelligence for resiliency and scale necessary for IoT. Ubiquity 11, 1–12 (2015)CrossRefGoogle Scholar
  28. 28.
    Edge Computing - Where data comes alive! https://vividcomm.com/2016/04/08/edge-computing-where-data-comes-alive/. Accessed Sept 2016
  29. 29.
    Greenberg, A., et al.: VL2: a scalable and flexible data center network. ACM SIGCOMM Comput. Commun. Rev. 39(4), 51–62 (2009)CrossRefGoogle Scholar
  30. 30.
    di Costanzo, A., de Assuncao, M.D., Buyya, R.: Harnessing cloud technologies for a virtualized distributed computing infrastructure. IEEE Internet Comput. 13(5), 24–33 (2009)CrossRefGoogle Scholar
  31. 31.
    Caraguay, V., Leonardo, A., et al.: SDN: evolution and opportunities in the development IoT applications. Int. J. Distrib. Sens. Netw., 1–10 (2014)Google Scholar
  32. 32.
    Blendin, J., et al.: Software-defined network service chaining. In: 2014 Third European Workshop on Software Defined Networks, pp. 109–114. IEEE, New York (2014)Google Scholar
  33. 33.
    Kim, H., Feamster, N.: Improving network management with software defined networking. IEEE Commun. Mag. 51(2), 114–119 (2013)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG 2016

Authors and Affiliations

  1. 1.Faculty of Computational Mathematics and CyberneticsLomonosov Moscow State UniversityMoscowRussia
  2. 2.Institute of Mathematics and Computer ScienceUniversity of LatviaRigaLatvia

Personalised recommendations