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.
Similar content being viewed by others
Change history
14 February 2024
This article has been retracted. Please see the Retraction Notice for more detail: https://doi.org/10.1007/s11227-024-05967-4
References
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
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
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
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
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
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
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
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
Varghese B, Nikolopoulos DS, Wang N (2017) Feasibility of fog computing. arXiv:1701.05451
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
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
Huang G-B, Zhu Q-Y, Siew C-K (2006) Extreme learning machine: theory and applications. Neuro Comput 70(1):489–501
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
Author information
Authors and Affiliations
Corresponding author
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
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
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11227-019-02993-5