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Internet of Nano-Things, Things and Everything: Future Growth Trends

Author

Listed:
  • Mahdi H. Miraz

    (Centre for Financial Regulation and Economic Development (CFRED), The Chinese University of Hong Kong, Sha Tin, Hong Kong, China)

  • Maaruf Ali

    (International Association of Educators and Researchers (IAER), Kemp House, 160 City Road, London EC1V 2NX, UK)

  • Peter S. Excell

    (Faculty of Art, Science and Technology, Wrexham Glyndŵr University, Wrexham LL11 2AW, UK)

  • Richard Picking

    (Faculty of Art, Science and Technology, Wrexham Glyndŵr University, Wrexham LL11 2AW, UK)

Abstract

The current statuses and future promises of the Internet of Things (IoT), Internet of Everything (IoE) and Internet of Nano-Things (IoNT) are extensively reviewed and a summarized survey is presented. The analysis clearly distinguishes between IoT and IoE, which are wrongly considered to be the same by many commentators. After evaluating the current trends of advancement in the fields of IoT, IoE and IoNT, this paper identifies the 21 most significant current and future challenges as well as scenarios for the possible future expansion of their applications. Despite possible negative aspects of these developments, there are grounds for general optimism about the coming technologies. Certainly, many tedious tasks can be taken over by IoT devices. However, the dangers of criminal and other nefarious activities, plus those of hardware and software errors, pose major challenges that are a priority for further research. Major specific priority issues for research are identified.

Suggested Citation

  • Mahdi H. Miraz & Maaruf Ali & Peter S. Excell & Richard Picking, 2018. "Internet of Nano-Things, Things and Everything: Future Growth Trends," Future Internet, MDPI, vol. 10(8), pages 1-28, July.
    • Handle: RePEc:gam:jftint:v:10:y:2018:i:8:p:68-:d:160566
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    Citations

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    Cited by:

    1. Md. Mahbubur Rahaman, 2022. "A Review on Internet of Things-IoT- Architecture, Technologies, Future Applications & Challenges," International Journal of Science and Business, IJSAB International, vol. 14(1), pages 80-92.
    2. Fabíola Martins Campos de Oliveira & Edson Borin, 2019. "Partitioning Convolutional Neural Networks to Maximize the Inference Rate on Constrained IoT Devices," Future Internet, MDPI, vol. 11(10), pages 1-30, September.

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