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Convergence of Information-Centric Networks and Edge Intelligence for IoV: Challenges and Future Directions

Author

Listed:
  • Salahadin Seid Musa

    (Department of Computer Science, Addis Ababa University, Addis Ababa P.O. Box 1176, Ethiopia
    STI Unit, Abdus Salam International Centre for Theoretical Physics, 34151 Trieste, Italy)

  • Marco Zennaro

    (STI Unit, Abdus Salam International Centre for Theoretical Physics, 34151 Trieste, Italy)

  • Mulugeta Libsie

    (Department of Computer Science, Addis Ababa University, Addis Ababa P.O. Box 1176, Ethiopia)

  • Ermanno Pietrosemoli

    (STI Unit, Abdus Salam International Centre for Theoretical Physics, 34151 Trieste, Italy)

Abstract

Recently the Internet of Vehicles (IoV) has become a promising research area in the field of the Internet of Things (IoT), which enables vehicles to communicate and exchange real-time information with each other, as well as with infrastructure, people, and other sensors and actuators through various communication interfaces. The realization of IoV networks faces various communication and networking challenges to meet stringent requirements of low latency, dynamic topology, high data-rate connectivity, resource allocation, multiple access, and QoS. Advances in information-centric networks (ICN), edge computing (EC), and artificial intelligence (AI) will transform and help to realize the Intelligent Internet of Vehicles (IIoV). Information-centric networks have emerged as a paradigm promising to cope with the limitations of the current host-based network architecture (TCP/IP-based networks) by providing mobility support, efficient content distribution, scalability and security based on content names, regardless of their location. Edge computing (EC), on the other hand, is a key paradigm to provide computation, storage and other cloud services in close proximity to where they are requested, thus enabling the support of real-time services. It is promising for computation-intensive applications, such as autonomous and cooperative driving, and to alleviate storage burdens (by caching). AI has recently emerged as a powerful tool to break through obstacles in various research areas including that of intelligent transport systems (ITS). ITS are smart enough to make decisions based on the status of a great variety of inputs. The convergence of ICN and EC with AI empowerment will bring new opportunities while also raising not-yet-explored obstacles to realize Intelligent IoV. In this paper, we discuss the applicability of AI techniques in solving challenging vehicular problems and enhancing the learning capacity of edge devices and ICN networks. A comprehensive review is provided of utilizing intelligence in EC and ICN to address current challenges in their application to IIoV. In particular, we focus on intelligent edge computing and networking, offloading, intelligent mobility-aware caching and forwarding and overall network performance. Furthermore, we discuss potential solutions to the presented issues. Finally, we highlight potential research directions which may illuminate efforts to develop new intelligent IoV applications.

Suggested Citation

  • Salahadin Seid Musa & Marco Zennaro & Mulugeta Libsie & Ermanno Pietrosemoli, 2022. "Convergence of Information-Centric Networks and Edge Intelligence for IoV: Challenges and Future Directions," Future Internet, MDPI, vol. 14(7), pages 1-31, June.
  • Handle: RePEc:gam:jftint:v:14:y:2022:i:7:p:192-:d:847955
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    References listed on IDEAS

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    1. Rute C. Sofia, 2019. "Guidelines towards Information-Driven Mobility Management," Future Internet, MDPI, vol. 11(5), pages 1-15, May.
    2. Marica Amadeo & Claudia Campolo & Antonella Molinaro & Jerome Harri & Christian Esteve Rothenberg & Alexey Vinel, 2019. "Enhancing the 3GPP V2X Architecture with Information-Centric Networking," Future Internet, MDPI, vol. 11(9), pages 1-19, September.
    3. Anselme Ndikumana & Saeed Ullah & Do Hyeon Kim & Choong Seon Hong, 2019. "DeepAuC: Joint deep learning and auction for congestion-aware caching in Named Data Networking," PLOS ONE, Public Library of Science, vol. 14(8), pages 1-26, August.
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    Cited by:

    1. Jiacheng Hou & Tianhao Tao & Haoye Lu & Amiya Nayak, 2023. "Intelligent Caching with Graph Neural Network-Based Deep Reinforcement Learning on SDN-Based ICN," Future Internet, MDPI, vol. 15(8), pages 1-20, July.

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