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Candidate architectures for emerging IoV: a survey and comparative study

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Abstract

Intelligent Transportation System (ITS) is observing significant evolution in terms of technology and investment worldwide. This has given birth to the new concept of Internet of vehicles (IoV) as one of the leading applications of the Internet of Things. IoV aims to offer a better sharing of information and communication between vehicles, enabling higher cooperation for common interests. IoV is increasingly attracting the interest of a significant body of research. The e ort was mostly focused on solving various problems encountered in traditional VANETs, such as lack of coordination between vehicles, insufficient information, scalability, etc. Rapidly, IoV observed, particularly interesting advances taking advantage of exponential growth in communication and data analysis technologies. This includes cloud and/or fog computing, large data analytics, machine learning, and artificial intelligence. In this paper, we make a survey of the existing and recently proposed architecture solutions for IoV systems. Moreover, we define a list of criteria, features, and properties associated to the various architectures in order of making critical and insightful comparisons and assessments. Finally, we outline the key future research perspectives on the topic and define the key technical aspects that will help drive the future of IoV architectures.

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Authors and Affiliations

  1. Physical and Natural Sciences of Tunis, Faculty of Mathematical, University of Tunis El Manar, 2092, Tunis, Tunisia

    Yamina Hichri

  2. Communication Engineering School (Sup’Com), LR11TIC05 MEDIATRON Lab, Faculty of Sciences of Bizerte, University of Carthage, 1083, Tunis, Tunisia

    Soumaya Dahi

  3. Faculty of Sciences of Bizerte, University of Carthage, Tunis, Tunisia

    Habib Fathallah

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  1. Yamina Hichri
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Correspondence to Yamina Hichri.

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Hichri, Y., Dahi, S. & Fathallah, H. Candidate architectures for emerging IoV: a survey and comparative study. Des Autom Embed Syst 25, 237–263 (2021). https://doi.org/10.1007/s10617-021-09249-7

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