IDEAS home Printed from https://ideas.repec.org/a/spr/ijsaem/v14y2023i3d10.1007_s13198-021-01523-y.html
   My bibliography  Save this article

Emerging technologies and design aspects of next generation cyber physical system with a smart city application perspective

Author

Listed:
  • Ayaskanta Mishra

    (KIIT Deemed to be University)

  • Amitkumar V. Jha

    (KIIT Deemed to be University)

  • Bhargav Appasani

    (KIIT Deemed to be University)

  • Arun Kumar Ray

    (KIIT Deemed to be University)

  • Deepak Kumar Gupta

    (KIIT Deemed to be University)

  • Abu Nasar Ghazali

    (KIIT Deemed to be University)

Abstract

The Cyber Physical System (CPS) is a disruptive technology that has combined the burgeoning technologies from various domains. The CPS is continuously evolving with the incorporation of next-generation technologies. A CPS capable of supporting next-generation applications is referred to as the Next Generation Cyber Physical System (NG-CPS). This paper comprehensively discusses the different emerging technologies such as Internet of Things, Machine to Machine communication, Machine Learning, Artificial Intelligence, Big-Data, etc. for the NG-CPS. Further, a generic NG-CPS framework is proposed covering all design aspects including physical design aspects, cyber design aspects and communication design aspects. Moreover, the smart city as a NG-CPS is designed using the proposed generic NG-CSP framework. To aid network designer in networking, the state-of-art protocols stack is also presented for smart city NG-CPS. Furthermore, to facilitate researchers in designing a smart city NG-CPS, the key technical specifications are comprehensively summarized, covering all domains of the NG-CPS.

Suggested Citation

  • Ayaskanta Mishra & Amitkumar V. Jha & Bhargav Appasani & Arun Kumar Ray & Deepak Kumar Gupta & Abu Nasar Ghazali, 2023. "Emerging technologies and design aspects of next generation cyber physical system with a smart city application perspective," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 14(3), pages 699-721, July.
    • Handle: RePEc:spr:ijsaem:v:14:y:2023:i:3:d:10.1007_s13198-021-01523-y
    DOI: 10.1007/s13198-021-01523-y
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s13198-021-01523-y
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s13198-021-01523-y?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Chaoyang Zhang & Zhengxu Wang & Kai Ding & Felix T.S. Chan & Weixi Ji, 2020. "An energy-aware cyber physical system for energy Big data analysis and recessive production anomalies detection in discrete manufacturing workshops," International Journal of Production Research, Taylor & Francis Journals, vol. 58(23), pages 7059-7077, December.
    2. Ritika Raj Krishna & Aanchal Priyadarshini & Amitkumar V. Jha & Bhargav Appasani & Avireni Srinivasulu & Nicu Bizon, 2021. "State-of-the-Art Review on IoT Threats and Attacks: Taxonomy, Challenges and Solutions," Sustainability, MDPI, vol. 13(16), pages 1-46, August.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Yin, Linfei & He, Xiaoyu, 2023. "Artificial emotional deep Q learning for real-time smart voltage control of cyber-physical social power systems," Energy, Elsevier, vol. 273(C).
    2. Xuan Su & Wenquan Dong & Jingyu Lu & Chen Chen & Weixi Ji, 2022. "Dynamic Allocation of Manufacturing Resources in IoT Job Shop Considering Machine State Transfer and Carbon Emission," Sustainability, MDPI, vol. 14(23), pages 1-21, December.
    3. Wang, Junya & Zhao, Qinfang & Ning, Ping & Wen, Shikun, 2024. "Greenhouse gas contribution and emission reduction potential prediction of China's aluminum industry," Energy, Elsevier, vol. 290(C).
    4. Mehmet Ali Soytaş & Damla Durak Uşar & Meltem Denizel, 2022. "Estimation of the static corporate sustainability interactions," International Journal of Production Research, Taylor & Francis Journals, vol. 60(4), pages 1245-1264, February.
    5. Bhargav Appasani & Sunil Kumar Mishra & Amitkumar V. Jha & Santosh Kumar Mishra & Florentina Magda Enescu & Ioan Sorin Sorlei & Fernando Georgel Bîrleanu & Noureddine Takorabet & Phatiphat Thounthong , 2022. "Blockchain-Enabled Smart Grid Applications: Architecture, Challenges, and Solutions," Sustainability, MDPI, vol. 14(14), pages 1-33, July.
    6. Xu, Jinou & Pero, Margherita & Fabbri, Margherita, 2023. "Unfolding the link between big data analytics and supply chain planning," Technological Forecasting and Social Change, Elsevier, vol. 196(C).
    7. Anwer Mustafa Hilal & Aisha Hassan Abdalla Hashim & Marwa Obayya & Abdulbaset Gaddah & Abdullah Mohamed & Ishfaq Yaseen & Mohammed Rizwanullah & Abu Sarwar Zamani, 2022. "Metaheuristics Based Energy Efficient Task Scheduling Scheme for Cyber-Physical Systems Environment," Sustainability, MDPI, vol. 14(24), pages 1-17, December.
    8. Fatima Rafiq & Mazhar Javed Awan & Awais Yasin & Haitham Nobanee & Azlan Mohd Zain & Saeed Ali Bahaj, 2022. "Privacy Prevention of Big Data Applications: A Systematic Literature Review," SAGE Open, , vol. 12(2), pages 21582440221, May.
    9. Dotun Adebanjo & Pei-Lee Teh & Pervaiz K Ahmed & Erhan Atay & Peter Ractham, 2020. "Competitive Priorities, Employee Management and Development and Sustainable Manufacturing Performance in Asian Organizations," Sustainability, MDPI, vol. 12(13), pages 1-22, July.
    10. Acciarini, Chiara & Cappa, Francesco & Boccardelli, Paolo & Oriani, Raffaele, 2023. "How can organizations leverage big data to innovate their business models? A systematic literature review," Technovation, Elsevier, vol. 123(C).

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:spr:ijsaem:v:14:y:2023:i:3:d:10.1007_s13198-021-01523-y. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.