Advertisement

IIoT Gateway for Edge Computing Applications

  • Mihai Crăciunescu
  • Oana ChenaruEmail author
  • Radu Dobrescu
  • Gheorghe Florea
  • Ştefan Mocanu
Conference paper
Part of the Studies in Computational Intelligence book series (SCI, volume 853)

Abstract

With the emergence of IoT applications Cloud architecture proves to be inefficient in handling massive amounts of data, mainly because of the variable latency and limited bandwidth. More specific, major requirements of Industrial Internet of Things (IIoT) like control and real-time decision making could not be addressed. These limitations along with the increasing intelligence in the lower levels of the data transmission architecture led to the development of an intermediate edge processing layer, closer to the process, enabling distributed computing and near real-time communication. In this paper a new perspective on edge architectures is presented and a model for a new edge gateway is designed. This device aims to facilitate new distributed computing methods while being able to handle both operational and functional requirements. Three case studies analyse how this device can be used to improve existing solutions: a hydroponic greenhouse, Smart Grid implementation for power systems and a video surveillance system in a manufacturing application.

Keywords

IIoT Edge computing Edge gateway Distributed computing 

Notes

Acknowledgment

This work was partially supported by the Romanian Ministry of Education and Research under grant 78PCCDI/2018-CIDSACTEH.

References

  1. 1.
    Bloom, G., Alsulami, B., Nwafor, E., Bertolotti, I.C.: Design patterns for the industrial internet of things. In: 14th IEEE International Workshop on Factory Communication Systems, pp. 1–10 (2018).  https://doi.org/10.1109/wfcs.2018.8402353
  2. 2.
    Sadiku, M.N.O., Wang, Y., Cui, S., Musa, S.M.: Industrial internet of things. Int. J. Eng. Res. Adv. Technol. 3(11), 1–5 (2017).  https://doi.org/10.7324/IJASRE.2017.32538CrossRefGoogle Scholar
  3. 3.
    IIC (Edge Computing Task Group), Introduction to Edge Computing in IIoT. White paper, pp. 1–19 (2018). https://www.iiconsortium.org/2018-06-18.pdf
  4. 4.
    El-Sayed, H., Sankar, S., Prasad, M., Puthal, D., Gupta, A., Mohanty, M., Lin, C.-T.: Edge of things: the big picture on the integration of edge, IoT and the cloud in a distributed computing environment. IEEE Access 6, 1706–1717 (2018).  https://doi.org/10.1109/ACCESS.2017.2780087CrossRefGoogle Scholar
  5. 5.
    Escamilla-Ambrosio, P.J., Rodríguez-Mota, A., Aguirre-Anaya, E., Acosta-Bermejo, R., Salinas-Rosales, M.: Distributing computing in the internet of things: cloud, fog and edge computing overview. In: Studies in Computational Intelligence, pp. 87–115 (2017).  https://doi.org/10.1007/978-3-319-64063-1_4Google Scholar
  6. 6.
    Liyanage, M., Chang, C., Srirama, S.N.: Adaptive mobile Web server framework for Mist computing in the IoT. Int. J. Pervasive Comput. Commun. 1–22 (2018).  https://doi.org/10.1108/ijpcc-d-18-00023CrossRefGoogle Scholar
  7. 7.
    Bangui, H., Rakrak, S., Raghay, S., Buhnova, B.: Moving to the edge-cloud-of-things: recent advances and future research directions. Electronics 7(11), 309–340 (2018).  https://doi.org/10.3390/electronics7110309CrossRefGoogle Scholar
  8. 8.
    Khan, I., Faisal, M.: Software-defined networking reviewed model. Int. J. Advancements Technol. 08(01), 1–5 (2017).  https://doi.org/10.4172/0976-4860.1000177CrossRefGoogle Scholar
  9. 9.
    Volpano, D.: Modular network function virtualization. In: IEEE Conference on Computer Communications Workshops, pp. 922–927 (2017).  https://doi.org/10.1109/infcomw.2017.8116499
  10. 10.
    Du, M., Wang, K., Chen, Y., Wang, X., Sun, Y.: Big data privacy preserving in multi-access edge computing for heterogeneous IoT. IEEE Commun. Mag. 56(8), 62–67 (2018).  https://doi.org/10.1109/MCOM.2018.1701148CrossRefGoogle Scholar
  11. 11.
    Li, H., Ota, K., Dong, M.: Learning IoT in edge: deep learning for the internet of things with edge computing. IEEE Netw. 32(1), 96–101 (2018).  https://doi.org/10.1109/MNET.2018.1700202CrossRefGoogle Scholar
  12. 12.
    Oyekanlu, E., Onidare, S., Oladele, P.: Towards statistical machine learning for edge analytics in large scale networks: realtime Gaussian function generation with generic DSP. In: First International Colloquium on Smart Grid Metrology, pp. 1–22 (2018).  https://doi.org/10.23919/smagrimet.2018.8369850
  13. 13.
    Chiti, F., Fantacci, R., Picano, B.: A matching theory framework for tasks offloading in fog computing for IoT systems. IEEE Internet Things J. 5(6), 5089–5096 (2018).  https://doi.org/10.1109/jiot.2018.2871251CrossRefGoogle Scholar
  14. 14.
    Kolomvatsos, K., Anagnostopoulos, C.: In-network decision making intelligence for task allocation in edge computing. In: 30th IEEE International Conference on Tools with Artificial Intelligence, pp. 655–662 (2018).  https://doi.org/10.1109/ictai.2018.00104
  15. 15.
    Sahni, Y., Cao, J., Yang, L.: Data-aware task allocation for achieving low latency in collaborative edge computing. IEEE Internet of Things J. PP(99), 1–13 (2018).  https://doi.org/10.1109/jiot.2018.2886757CrossRefGoogle Scholar
  16. 16.
    Song, Y., Yau, S.S., Yu, R., Zhang, X., Xue, G.: An approach to QoS-based task distribution in edge computing networks for IoT apps. In: IEEE International Conference on Edge Computing, pp. 32–39 (2017).  https://doi.org/10.1109/ieee.edge.2017.50
  17. 17.
    Bloom, G., Alsulami, B., Nwafor, E., Bertolotti, I.C.: Design patterns for the industrial internet of things. In: 2018 14th IEEE International Workshop on Factory Communication Systems, pp. 1–10 (2018)Google Scholar
  18. 18.
    Jridi, M., Chapel, T., Dorez, V., Le Bougeant, G., Le Botlan, A.: SoC-based edge computing gateway in the context of the internet of multimedia things: experimental platform. J. Low Power Electron. Appl. 8(1), 1–18 (2018).  https://doi.org/10.3390/jlpea8010001CrossRefGoogle Scholar
  19. 19.
    Nuratch, S.: The IIoT devices to cloud gateway design and implementation based on microcontroller for real-time monitoring and control in automation systems. In: 12th IEEE Conference on Industrial Electronics and Applications, pp. 919–923 (2017).  https://doi.org/10.1109/iciea.2017.8282970
  20. 20.
    Shah, N., Bhatt, C., Patel, D.: IoT gateway for smart devices, internet of things and big data analytics toward next-generation. Intelligence 30, 179–198 (2017).  https://doi.org/10.1007/978-3-319-60435-0CrossRefGoogle Scholar
  21. 21.
    Vapor IO. State of the Edge 2018 - A Market and Ecosystem Report for Edge Computing. https://www.vapor.io/wp-content/uploads/2018/09/State-of-the-Edge-2018.pdf
  22. 22.
    Mocanu, Ş., Dumitraşcu, A., Popa, C.: Complex system dedicated to monitoring and control of hydroponic greenhouse environment. In: International Multidisciplinary Scientific Geo Conference: SGEM: Surveying Geology and Mining Ecology Management, vol. 17, pp. 243–255 (2017). ISSN: 1314-2704,  https://doi.org/10.5593/sgem2017/51/s20.032
  23. 23.
    Florea, G., Chenaru, O., Popescu, D., Dobrescu, R.: Evolution from power grid to smart grid: design challenges. In: 19th International Conference on System Theory, Control and Computing (ICSTCC), pp. 912–916 (2015). ISBN: 978-1-4799-8480-0,  https://doi.org/10.1109/icstcc.2015.7321411

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  • Mihai Crăciunescu
    • 1
  • Oana Chenaru
    • 1
    Email author
  • Radu Dobrescu
    • 1
  • Gheorghe Florea
    • 1
  • Ştefan Mocanu
    • 1
  1. 1.Department of Automation and Industrial InformaticsUniversity Politehnica of BucharestBucharestRomania

Personalised recommendations