Advertisement

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

  • S. KarthikeyanEmail author
  • K. Vimala Devi
  • K. Valarmathi
Article
  • 3 Downloads

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.

Keywords

CfoTS FAN IoT Machine learning algorithms MQTT Cognitive learning fogs 

Notes

References

  1. 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):4548–4556CrossRefGoogle Scholar
  2. 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 125–128Google Scholar
  3. 3.
    Bader A, Ghazzai H, Alouini M-S, Kadri A (2016) Front-end intelligence for large-scale application-oriented internet-of-things. IEEE Access 4:3257–3272CrossRefGoogle Scholar
  4. 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 8:1–10Google Scholar
  5. 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 1–7Google Scholar
  6. 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 1138–1142Google Scholar
  7. 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 41–50Google Scholar
  8. 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 367–374Google Scholar
  9. 9.
    Varghese B, Nikolopoulos DS, Wang N (2017) Feasibility of fog computing. arXiv:1701.05451
  10. 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 6:11278–11288CrossRefGoogle Scholar
  11. 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):52–58CrossRefGoogle Scholar
  12. 12.
    Huang G-B, Zhu Q-Y, Siew C-K (2006) Extreme learning machine: theory and applications. Neuro Comput 70(1):489–501Google Scholar
  13. 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 52–57Google Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Department of Electronics and Instrumentation EngineeringM. Kumarasamy College of EngineeringKarurIndia
  2. 2.School of Computer Science and EngineeringVellore Institute of TechnologyVelloreIndia
  3. 3.Department of Electronics and Communication EngineeringP.S.R. Engineering CollegeSivakasiIndia

Personalised recommendations