Handling research issues for big data extraction in the application of Internet of Vehicles (IoV)

Gurpreet Singh Panesar (), Kuldeep Narayan Tripathi (), Jyoti L. Bangare (), Rahul Neware () and Skanda Moda Gururajarao ()
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Gurpreet Singh Panesar: Chandigarh University
Kuldeep Narayan Tripathi: Indian Institute of Technology Roorkee
Jyoti L. Bangare: Savitribai Phule Pune University
Rahul Neware: Høgskulen på Vestlandet
Skanda Moda Gururajarao: SJCE, JSS Science and Technology University

International Journal of System Assurance Engineering and Management, 2022, vol. 13, issue 1, No 76, 756 pages

Abstract: Abstract Big data is becoming increasingly important in the Internet of Vehicles due to the quick expansion of the Vehicular internet infrastructure as well as the dramatic rise of information units. Big data is receiving a great deal of interest in academia and industries. It substantially assists in the formulation of accurate selections as well as the growth of the firm and industry. Furthermore, data from connected vehicles was seen and public participation in advance area development may benefit from enhanced control. The purpose of this study is to provide a detailed overview of all types of self-review articles generated in the early years. We organized a detailed assessment of the research articles for the purpose of discovering possibilities. As a consequence, the study illustrates how big data may help provide accurate and relevant projections and also a comprehensive review of various techniques, gadgets, and methods for using information in the vehicular IN. This research work introduces the pros and corns of various research works in the field of vehicular internet along with the methodology proposed in these research works. The paper focuses on extraction and decomposing lot of information related to traffic of vehicle interneting.

Keywords: Big data processing and analysis; Traffic congestion; Internet automobiles; Connected vehicles (search for similar items in EconPapers)
Date: 2022
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DOI: 10.1007/s13198-021-01607-9

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