Skip to main content
SpringerLink
Log in

A CPS-Based IIoT Architecture Using Level Diagnostics Model for Smart Factory

  • Conference paper
  • First Online:
Book cover Computational Science and Its Applications – ICCSA 2020 (ICCSA 2020)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 12250))

Included in the following conference series:

Abstract

In this paper, a construction process using a level diagnostic agent was applied to the construction of a smart factory. The current status of the smart factory of the demanding company was measured and the target level was derived, and a cps-based design of the smart factory construction type was proposed. It is suggested that the construction of a CPS simulation based smart factory is more effective in preparation for cloud based smart factory manufacturing in the process of informatization, automation, and intelligence of the smart factory due to the explosive increase of data. In this paper, a Korean-type smart factory adopting an empirical research method that activates the actual construction cases of smart factory level diagnosis according to the basic components of the smart factory, information, automation, and intelligence, and the present examples of each smart factory level as an empirical case.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 84.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 109.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Zawadzki, P., Żywicki, K.: Smart product design and production control for effective mass customization in the industry 4.0 concept. Manage. Prod. Eng. Rev. 7(3), 105–112 (2016)

    Google Scholar 

  2. Wan, J., Yin, B., Li, D., Celesti, A., Tao, F., Hua, Q.: An ontology-based resource reconfiguration method for manufacturing cyber-physical systems. IEEE/ASME Trans. Mechatron. 23(6), 2537–2546 (2018)

    Article  Google Scholar 

  3. Chen, B., Wan, J., Shu, L., Li, P., Mukherjee, M., Yin, B.: Smart factory of industry 4.0: key technologies, application case, and challenges. IEEE Access 6, 6505–6519 (2017)

    Article  Google Scholar 

  4. Wang, S., et al.: Implementing smart factory of industry 4.0: an outlook. Int. J. Distrib. Sens. Netw. 12(1), 3159805 (2016)

    Article  Google Scholar 

  5. Wollschlaeger, M., Sauter, T., Jasperneite, J.: The future of industrial communication: automation networks in the era of the internet of things and industry 4.0. IEEE Ind. Electron. Mag. 11(1), 17–27 (2017)

    Article  Google Scholar 

  6. Wan, J., et al.: Reconfigurable smart factory for drug packing in healthcare industry 4.0. IEEE Trans. Ind. Informat. 15(1), 507–516 (2019)

    Article  Google Scholar 

  7. Wu, D., Rosen, D.W., Wang, L., Schaefer, D.: Cloud-based design and manufacturing: a new paradigm in digital manufacturing and design innovation. Comput. Aided Des. 59, 1–14 (2015)

    Article  Google Scholar 

  8. Cui, A., Stolfo, S.J.: A quantitative analysis of the insecurity of embedded network devices: results of a wide-area scan. In: Proceedings of the 26th Annual Computer Security Applications Conference, Austin, TX, USA, pp. 97–106 (2010)

    Google Scholar 

  9. Colombo, A.W., et al. (eds.): Industrial Cloud-Based Cyber-Physical Systems. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-05624-1

    Book  Google Scholar 

  10. O’Donovan, P., Leahy, K., Bruton, K., O’Sullivan, D.T.J.: An industrial big data pipeline for data-driven analytics maintenance applications in large-scale smart manufacturing facilities. J. Big Data 2(1), 1–26 (2015). https://doi.org/10.1186/s40537-015-0034-z

  11. Herwan, J., Kano, S., Oleg, R., Sawada, H., Kasashima, N.: Cyber-physical system architecture for machining production line. In: 2018 IEEE Industrial Cyber-Physical Systems (ICPS), St. Petersburg, pp. 387-391 (2018)

    Google Scholar 

  12. Felser, M., Rentschler, M., Kleineberg, O.: Coexistence standardization of operation technology and information technology. In: Proceedings of the IEEE Proceedings IEEE Proceedings of the IEEE, vol. 107, no. 6, pp. 962–976, June 2019

    Google Scholar 

  13. Lau, J.K.S., Tham, C.K., Luo, T.: Participatory cyber physical system in public transport application. In: IEEE International Conference on Utility and Cloud Computing, Victoria, NSW, pp. 355–360 (2011)

    Google Scholar 

Download references

Acknowledgment

This research was supported by Basic Science Re-search Program through the National Research Foundation of Korea(NRF) funded by the Ministry of Education (NRF-2017R1A6A3A11035613).

Author information

Authors and Affiliations

  1. Department of Smart Factory Convergence, Sungkyunkwan University, Suwon, Gyeonggi-do, 16419, Republic of Korea

    Byungjun Park & Jongpil Jeong

Authors
  1. Byungjun Park
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Jongpil Jeong
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Jongpil Jeong .

Editor information

Editors and Affiliations

  1. University of Perugia, Perugia, Italy

    Osvaldo Gervasi

  2. University of Basilicata, Potenza, Potenza, Italy

    Beniamino Murgante

  3. Chair- Center of ICT/ICE, Covenant University, Ota, Nigeria

    Sanjay Misra

  4. University of Cagliari, Cagliari, Italy

    Chiara Garau

  5. University of Cagliari, Cagliari, Italy

    Ivan Blečić

  6. Clayton School of Information Technology, Monash University, Clayton, VIC, Australia

    David Taniar

  7. Department of Information Science, Kyushu Sangyo University, Fukuoka, Japan

    Bernady O. Apduhan

  8. University of Minho, Braga, Portugal

    Ana Maria A.C. Rocha

  9. Polytechnic University of Bari, Bari, Italy

    Eufemia Tarantino

  10. Polytechnic University of Bari, Bari, Italy

    Carmelo Maria Torre

  11. Department of Neurology, University of Massachusetts Medical School, Worcester, MA, USA

    Yeliz Karaca

Rights and permissions

Reprints and permissions

Copyright information

© 2020 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Park, B., Jeong, J. (2020). A CPS-Based IIoT Architecture Using Level Diagnostics Model for Smart Factory. In: Gervasi, O., et al. Computational Science and Its Applications – ICCSA 2020. ICCSA 2020. Lecture Notes in Computer Science(), vol 12250. Springer, Cham. https://doi.org/10.1007/978-3-030-58802-1_41

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-58802-1_41

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-58801-4

  • Online ISBN: 978-3-030-58802-1

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation