Skip to main content

Advertisement

SpringerLink
Log in

RETRACTED ARTICLE: Design and implementation of CfoTS networks for industrial fault detection and correction mechanism

  • Published:
The Journal of Supercomputing Aims and scope Submit manuscript

This article was retracted on 14 February 2024

This article has been updated

Abstract

In the industry, large- and small-scale manufacturers and even original equipment manufacturers are facing a major problem in monitoring large data. Because the amount of data is increasing daily, detecting faults and the methodology of detecting faults are becoming increasingly complex, such that there are insufficient intelligent data-driven mechanisms for achieving a short response time and high accuracy. Intelligent systems utilizing the advantages of Internet of Things (IoT) are emerging; however, they still require innovation. To design an intelligent system for a fault detection system, we propose a new fog-based IoT framework called cognitive Fog of Things framework, for achieving improved industrial fault detection and correction. The proposed framework comprises fog area networks including sensor nodes, fogs, and machine learning algorithms for detection and prediction. The proposed network operates on message queue transportation telemetry and cognitive learning fogs. The proposed concept is developed in a real-time scenario using Raspberry Pi with different case studies for implementation and using various parameters such as different types of faults, time of computation, detection time, and accuracy.

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

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14

Similar content being viewed by others

Change history

References

  1. Wan J, Wang S, Chen B, Li D, Xia M, Liu C (2018) Fog computing for energy-aware load balancing and scheduling in smart factory. IEEE Trans Ind Inf 14(10):4548–4556

    Article  Google Scholar 

  2. Stolfo SJ, Keromytis AD, Salem MB (2012) Fog computing: mitigating insider data theft attacks in the cloud. In: IEEE Symposium on Security and Privacy Workshops, pp 125–128

  3. Bader A, Ghazzai H, Alouini M-S, Kadri A (2016) Front-end intelligence for large-scale application-oriented internet-of-things. IEEE Access 4:3257–3272

    Article  Google Scholar 

  4. Verma M, Yadav AK, Bhardwaj N (2016) Real time efficient scheduling algorithm (ESA) for load balancing in fog computing environment. Int J Inf Technol Comput Sci 8:1–10

    Google Scholar 

  5. Agrawal H, Mane TS (2017) Cloud fog dew architecture for refined driving assistance: the complete service computing ecosystem. In: IEEE International Conference on Ubiquitous Wireless Broadband, pp 1–7

  6. Xiao J, Kou P (2017) A hierarchical distributed fault diagnosis system for hydropower plant based on fog computing. In: IEEE Conference on Information Technology, Networking, Electronic and Automation Control, pp 1138–1142

  7. Confais B, Parrein B, Lebre A (2017) An object store service for a fog/edge computing infrastructure based on IPFS and scale-out NAS. In: IEEE Conference on Fog and Edge Computing, pp 41–50

  8. Wenger R, Krishnamurthy J, Zhu X, Maheswaran M (2016) A programming language and system for heterogeneous cloud of things. In: IEEE 2nd International Conference on Collaboration and Internet Computing, pp 367–374

  9. Varghese B, Nikolopoulos DS, Wang N (2017) Feasibility of fog computing. arXiv:1701.05451

  10. Li G, Guan Z, Guo L, Wu J, Li J (2018) Fog computing enabled secure demand response for internet of energy against collusion attacks using consensus and ACE. IEEE Access 6:11278–11288

    Article  Google Scholar 

  11. Akyildiz IF, Lee A, Chou W, Wang P, Luo M (2016) Research challenges for traffic engineering in software defined network’s. IEEE Netw 30(3):52–58

    Article  Google Scholar 

  12. Huang G-B, Zhu Q-Y, Siew C-K (2006) Extreme learning machine: theory and applications. Neuro Comput 70(1):489–501

    Google Scholar 

  13. Lian C, Zeng Z, Yao W, Tang H (2013) Displacement prediction of landslide based on psogsa-elm with mixed kernel. In: Sixth International Conference on Advanced Computational Intelligence (ICACI). IEEE, pp 52–57

Download references

Author information

Authors and Affiliations

  1. Department of Electronics and Instrumentation Engineering, M. Kumarasamy College of Engineering, Karur, Tamil Nadu, 639113, India

    S. Karthikeyan

  2. School of Computer Science and Engineering, Vellore Institute of Technology, Vellore, Tamil Nadu, 632014, India

    K. Vimala Devi

  3. Department of Electronics and Communication Engineering, P.S.R. Engineering College, Sivakasi, Tamil Nadu, 626140, India

    K. Valarmathi

Authors
  1. S. Karthikeyan
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. K. Vimala Devi
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. K. Valarmathi
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to S. Karthikeyan.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

This article has been retracted. Please see the retraction notice for more detail: https://doi.org/10.1007/s11227-024-05967-4"

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Karthikeyan, S., Vimala Devi, K. & Valarmathi, K. RETRACTED ARTICLE: Design and implementation of CfoTS networks for industrial fault detection and correction mechanism. J Supercomput 76, 5763–5779 (2020). https://doi.org/10.1007/s11227-019-02993-5

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11227-019-02993-5

Keywords

Search

Navigation