IDEAS home Printed from https://ideas.repec.org/a/hin/jnlmpe/9022558.html
   My bibliography  Save this article

Deep Learning-Based Big Data Analytics for Internet of Vehicles: Taxonomy, Challenges, and Research Directions

Author

Listed:
  • Haruna Chiroma
  • Shafi’i M. Abdulhamid
  • Ibrahim A. T. Hashem
  • Kayode S. Adewole
  • Absalom E. Ezugwu
  • Saidu Abubakar
  • Liyana Shuib

Abstract

The Internet of Vehicles (IoV) is a developing technology attracting attention from the industry and the academia. Hundreds of millions of vehicles are projected to be connected within the IoV environments by 2035. Each vehicle in the environment is expected to generate massive amounts of data. Currently, surveys on leveraging deep learning (DL) in the IoV within the context of big data analytics (BDA) are scarce. In this paper, we present a survey and explore the theoretical perspective of the role of DL in the IoV within the context of BDA. The study has unveiled substantial research opportunities that cut across DL, IoV, and BDA. Exploring DL in the IoV within BDA is an infant research area requiring active attention from researchers to fully understand the emerging concept. The survey proposes a model of IoV environment integrated into the cloud equipped with a high-performance computing server, DL architecture, and Apache Spark for data analytics. The current developments, challenges, and opportunities for future research are presented. This study can guide expert and novice researchers on further development of the application of DL in the IoV within the context of BDA.

Suggested Citation

  • Haruna Chiroma & Shafi’i M. Abdulhamid & Ibrahim A. T. Hashem & Kayode S. Adewole & Absalom E. Ezugwu & Saidu Abubakar & Liyana Shuib, 2021. "Deep Learning-Based Big Data Analytics for Internet of Vehicles: Taxonomy, Challenges, and Research Directions," Mathematical Problems in Engineering, Hindawi, vol. 2021, pages 1-20, November.
    • Handle: RePEc:hin:jnlmpe:9022558
    DOI: 10.1155/2021/9022558
    as

    Download full text from publisher

    File URL: http://downloads.hindawi.com/journals/MPE/2021/9022558.pdf
    Download Restriction: no
    File URL: http://downloads.hindawi.com/journals/MPE/2021/9022558.xml
    Download Restriction: no
    File URL: https://libkey.io/10.1155/2021/9022558?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
    ---><---

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Hemavathi & Sreenatha Reddy Akhila & Youseef Alotaibi & Osamah Ibrahim Khalaf & Saleh Alghamdi, 2022. "Authentication and Resource Allocation Strategies during Handoff for 5G IoVs Using Deep Learning," Energies, MDPI, vol. 15(6), pages 1-27, March.

    More about this item

    Statistics

    Access and download statistics

    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:hin:jnlmpe:9022558. 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.

    We have no bibliographic references for this item. You can help adding them by using 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: Mohamed Abdelhakeem (email available below). General contact details of provider: https://www.hindawi.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.