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A Systematic Review on Cognitive Radio in Low Power Wide Area Network for Industrial IoT Applications

Author

Listed:
  • Nahla Nurelmadina

    (Department of Computer Science and Engineering, Taibah University, Tayba, Medina 42353, Saudi Arabia)

  • Mohammad Kamrul Hasan

    (Center for Cyber Security, Faculty of Information Science and Technology, Universiti Kebangsaan Malaysia, Bangi 43600, Malaysia)

  • Imran Memon

    (Department of Computer Science, Bahria University Karachi Campus, Karachi 75260, Pakistan)

  • Rashid A. Saeed

    (Department of Computer Engineering, Taif University, Taif 21944, Saudi Arabia
    Department of Electronics Engineering, Sudan University of Science and Technology (SUST), Khartoum 11111, Sudan)

  • Khairul Akram Zainol Ariffin

    (Center for Cyber Security, Faculty of Information Science and Technology, Universiti Kebangsaan Malaysia, Bangi 43600, Malaysia)

  • Elmustafa Sayed Ali

    (Department of Electrical Engineering, Red Sea University, Port Sudan 34875, Sudan)

  • Rania A. Mokhtar

    (Department of Computer Engineering, Taif University, Taif 21944, Saudi Arabia
    Department of Electronics Engineering, Sudan University of Science and Technology (SUST), Khartoum 11111, Sudan)

  • Shayla Islam

    (Department of Computer Science, Institute of Computer Science and Digital Innovation, UCSI University, Kuala Lumpur 56000, Malaysia)

  • Eklas Hossain

    (Department of Electrical Engineering and Renewable Energy, Oregon Institute of Technology, Klamath Falls, OR 97601, USA)

  • Md. Arif Hassan

    (Center for Cyber Security, Faculty of Information Science and Technology, Universiti Kebangsaan Malaysia, Bangi 43600, Malaysia)

Abstract

The Industrial Internet of things (IIoT) helps several applications that require power control and low cost to achieve long life. The progress of IIoT communications, mainly based on cognitive radio (CR), has been guided to the robust network connectivity. The low power communication is achieved for IIoT sensors applying the Low Power Wide Area Network (LPWAN) with the Sigfox, NBIoT, and LoRaWAN technologies. This paper aims to review the various technologies and protocols for industrial IoT applications. A depth of assessment has been achieved by comparing various technologies considering the key terms such as frequency, data rate, power, coverage, mobility, costing, and QoS. This paper provides an assessment of 64 articles published on electricity control problems of IIoT between 2007 and 2020. That prepares a qualitative technique of answering the research questions (RQ): RQ1: “How cognitive radio engage with the industrial IoT?”, RQ2: “What are the Proposed architectures that Support Cognitive Radio LPWAN based IIOT?”, and RQ3: What key success factors need to comply for reliable CIIoT support in the industry?”. With the systematic literature assessment approach, the effects displayed on the cognitive radio in LPWAN can significantly revolute the commercial IIoT. Thus, researchers are more focused in this regard. The study suggests that the essential factors of design need to be considered to conquer the critical research gaps of the existing LPWAN cognitive-enabled IIoT. A cognitive low energy architecture is brought to ensure efficient and stable communications in a heterogeneous IIoT. It will protect the network layer from offering the customers an efficient platform to rent AI, and various LPWAN technology were explored and investigated.

Suggested Citation

  • Nahla Nurelmadina & Mohammad Kamrul Hasan & Imran Memon & Rashid A. Saeed & Khairul Akram Zainol Ariffin & Elmustafa Sayed Ali & Rania A. Mokhtar & Shayla Islam & Eklas Hossain & Md. Arif Hassan, 2021. "A Systematic Review on Cognitive Radio in Low Power Wide Area Network for Industrial IoT Applications," Sustainability, MDPI, vol. 13(1), pages 1-20, January.
    • Handle: RePEc:gam:jsusta:v:13:y:2021:i:1:p:338-:d:473525
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    References listed on IDEAS

    as
    1. Mohammad Kamrul Hasan & Ahmad Fadzil Ismail & Shayla Islam & Wahidah Hashim & Musse Mohamud Ahmed & Imran Memon, 2019. "A Novel HGBBDSA-CTI Approach for Subcarrier Allocation in Heterogeneous Network," Telecommunication Systems: Modelling, Analysis, Design and Management, Springer, vol. 70(2), pages 245-262, February.
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    Cited by:

    1. Taher M. Ghazal & Mohammad Kamrul Hasan & Muhammad Turki Alshurideh & Haitham M. Alzoubi & Munir Ahmad & Syed Shehryar Akbar & Barween Al Kurdi & Iman A. Akour, 2021. "IoT for Smart Cities: Machine Learning Approaches in Smart Healthcare—A Review," Future Internet, MDPI, vol. 13(8), pages 1-19, August.

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