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IoTIwC: IoT Industrial Wireless Controller

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Machine Learning and Big Data Analytics Paradigms: Analysis, Applications and Challenges

Part of the book series: Studies in Big Data ((SBD,volume 77))

Abstract

Industrial controller systems are crucially essential to the cutting edge power systems industries. Industrial controllers link the integrated technologies of a computer, communication devices, and electric devices. The communication systems act as a physical intermediary layer for transferring, controlling, and acquirement of data within the system from distant locations. This chapter discusses the Supervisory Control And Data Acquisition (SCADA) systems and proposes a similar system that is an IoT based industrial wireless controller. The proposed system can control multiple devices through the network without the need to be physically near the devices. Because it uses simple and cheap devices, the system is low cost and easy to install. Additionally, the system is modular because extra microcontrollers can be easily added to the system to control more devices should the need arise.

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Authors and Affiliations

  1. Electrical Engineering Department, The British University in Egypt, Cairo, Egypt

    Tassnim Awad, Walaa Mohamed & Mohammad M. Abdellatif

Authors
  1. Tassnim Awad
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  2. Walaa Mohamed
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  3. Mohammad M. Abdellatif
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Corresponding author

Correspondence to Mohammad M. Abdellatif .

Editor information

Editors and Affiliations

  1. Faculty of Computer and Artificial Intelligence, Cairo University, Giza, Egypt

    Aboul Ella Hassanien

  2. Faculty of Science, Helwan University, Helwan, Egypt

    Ashraf Darwish

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Awad, T., Mohamed, W., Abdellatif, M.M. (2021). IoTIwC: IoT Industrial Wireless Controller. In: Hassanien, A.E., Darwish, A. (eds) Machine Learning and Big Data Analytics Paradigms: Analysis, Applications and Challenges. Studies in Big Data, vol 77. Springer, Cham. https://doi.org/10.1007/978-3-030-59338-4_27

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  • DOI: https://doi.org/10.1007/978-3-030-59338-4_27

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-59337-7

  • Online ISBN: 978-3-030-59338-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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