Machine Learning for Industrial Internet of Things (IIoT)

Submission Deadline: 31 January 2023 (closed)

Guest Editors

Dr. Jawad Ahmad, Edinburgh Napier University, UK.
Dr. Arshad Arshad, University of Strathclyde, UK.
Dr. Syed Aziz Shah, Coventry University, UK.
Dr. Nick Pitropakis, Edinburgh Napier University, UK.


The Internet of Things (IoT) is a term that refers to widespread connectivity between everyday devices and the Internet. IoT works by deploying thousands of smart devices in residential and industrial settings. These devices gather data from their surroundings, conduct desired processing operations on the data, and send the processed data via secure, dependable communication channels. In terms of time, energy, and cost savings, recent developments in software, hardware, and communication technologies have considerably enhanced human lifestyles. The phrase "Industrial Internet of Things" (IIoT) refers to the implementation of traditional Internet of Things concepts in industrial settings. In an industrial setting, the IIoT optimises manufacturing methods by enabling sustainable and efficient solutions. The IIoT market is currently experiencing tremendous expansion, as well as increased adaptability in many sectors' digital transitions. Strong partnerships and common interests among IIoT stakeholders and developing applications have enticed large corporations from around the world to engage in this growing market. In this regard, we encourage academics to submit original research articles as well as review articles that will aim to explore novel machine learning-based techniques in the area of Industrial IoT.


Machine Learning, IoT, Cybersecurity, Multimedia Encryption, Classification, Smart Systems, Cybersecurity

Published Papers

  • Open Access


    An Efficient IIoT-Based Smart Sensor Node for Predictive Maintenance of Induction Motors

    Majida Kazmi, Maria Tabasum Shoaib, Arshad Aziz, Hashim Raza Khan, Saad Ahmed Qazi
    Computer Systems Science and Engineering, Vol.47, No.1, pp. 255-272, 2023, DOI:10.32604/csse.2023.038464
    (This article belongs to this Special Issue: Machine Learning for Industrial Internet of Things (IIoT))
    Abstract Predictive maintenance is a vital aspect of the industrial sector, and the use of Industrial Internet of Things (IIoT) sensor nodes is becoming increasingly popular for detecting motor faults and monitoring motor conditions. An integrated approach for acquiring, processing, and wirelessly transmitting a large amount of data in predictive maintenance applications remains a significant challenge. This study presents an IIoT-based sensor node for industrial motors. The sensor node is designed to acquire vibration data on the radial and axial axes of the motor and utilizes a hybrid approach for efficient data processing via edge and cloud platforms. The initial step… More >

  • Open Access


    Network Learning-Enabled Sensor Association for Massive Internet of Things

    Alaa Omran Almagrabi, Rashid Ali, Daniyal Alghazzawi, Bander A. Alzahrani, Fahad M. Alotaibi
    Computer Systems Science and Engineering, Vol.47, No.1, pp. 843-853, 2023, DOI:10.32604/csse.2023.037652
    (This article belongs to this Special Issue: Machine Learning for Industrial Internet of Things (IIoT))
    Abstract The massive Internet of Things (IoT) comprises different gateways (GW) covering a given region of a massive number of connected devices with sensors. In IoT networks, transmission interference is observed when different sensor devices (SD) try to send information to a single GW. This is mitigated by allotting various channels to adjoining GWs. Furthermore, SDs are permitted to associate with any GW in a network, naturally choosing the one with a higher received signal strength indicator (RSSI), regardless of whether it is the ideal choice for network execution. Finding an appropriate GW to optimize the performance of IoT systems is… More >

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