skip to main content
10.1145/3397166.3413465acmconferencesArticle/Chapter ViewAbstractPublication PagesmobihocConference Proceedingsconference-collections
research-article

How to deal with data hungry V2X applications?

Published:11 October 2020Publication History

ABSTRACT

Current vehicular communication technologies were designed for a so-called phase 1, where cars needed to advise of their presence. Several projects, research activities and field tests have proved their effectiveness to this scope. But entering the phase 2, where awareness needs to be improved with non-connected objects and vulnerable road users, and even more with phases 3 and 4, where also coordination is foreseen, the spectrum scarcity becomes a critical issue. In this work, we provide an overview of various 5G and beyond solutions currently under investigation that will be needed to tackle the challenge. We first recall the undergoing activities at the access layer aimed to satisfy capacity and bandwidth demands. We then discuss the role that emerging networking paradigms can play to improve vehicular data dissemination, while preventing congestion and better exploiting resources. Finally, we give a look into edge computing and machine learning techniques that will be determinant to efficiently process and mine the massive amounts of sensor data.

References

  1. 2019. 3GPP TR 22.186 V16.2.0, Technical Specification Group Services and System Aspects. Enhancement of 3GPP Support for V2X Scenarios. Release 15.Google ScholarGoogle Scholar
  2. A.D. Angelica. 2013 (accessed July 8, 2020). Google's self-driving car gathers nearly 1 GB/sec. https://www.kurzweilai.net/googles-self-driving-car-gathers-nearly-1-gbsecGoogle ScholarGoogle Scholar
  3. C. Campolo, A. Molinaro, A.O. Berthet, and A. Vinel. 2017. Full-duplex radios for vehicular communications. IEEE Communications Magazine 55, 6 (2017), 182--189.Google ScholarGoogle ScholarDigital LibraryDigital Library
  4. Car 2 Car Communication Consortium et al. 2011. Memorandum of Understanding for OEMs within the CAR 2 CAR Communication Consortium on Deployment Strategy for cooperative ITS in Europe. C2C-CC Final Version 4, 02 (2011), 1--5.Google ScholarGoogle Scholar
  5. M. Di Renzo et al. 2019. Smart radio environments empowered by reconfigurable AI meta-surfaces: An idea whose time has come. EURASIP Journal on Wireless Communications and Networking 2019, 1 (2019).Google ScholarGoogle ScholarCross RefCross Ref
  6. A.M. Elbir and S. Coleri. 2020. Federated Learning for Vehicular Networks. arXiv preprint arXiv:2006.01412 (2020).Google ScholarGoogle Scholar
  7. R. Fukatsu and K. Sakaguchi. 2019. Millimeter-Wave V2V Communications with Cooperative Perception for Automated Driving. In 2019 IEEE 89th Vehicular Technology Conference (VTC2019-Spring). 1--5.Google ScholarGoogle Scholar
  8. M. Giordani et al. 2019. Investigating Value of Information in Future Vehicular Communications. In 2019 IEEE Connected and Automated Vehicles Symposium (CAVS). 1--5.Google ScholarGoogle Scholar
  9. H. Günther et al. 2016. Realizing collective perception in a vehicle. In 2016 IEEE Vehicular Networking Conference (VNC). 1--8.Google ScholarGoogle ScholarCross RefCross Ref
  10. R. He et al. 2020. Propagation Channels of 5G Millimeter-Wave Vehicle-to-Vehicle Communications: Recent Advances and Future Challenges. IEEE Vehicular Technology Magazine 15, 1 (2020), 16--26.Google ScholarGoogle ScholarCross RefCross Ref
  11. Z. He, D. Zhang, and J. Liang. 2016. Cost-efficient sensory data transmission in heterogeneous software-defined vehicular networks. IEEE Sensors Journal 16, 20 (2016), 7342--7354.Google ScholarGoogle ScholarCross RefCross Ref
  12. H. Huang et al. 2020. Data Redundancy Mitigation in V2X Based Collective Perceptions. IEEE Access 8 (2020), 13405--13418.Google ScholarGoogle ScholarCross RefCross Ref
  13. ETSI ITS. 2019. Intelligent Transport System (ITS); Vehicular Communications; Basic Set of Applications; Analysis of the Collective Perception Service (CPS); Release 2. ETSI TR 103 562 V2.1.1 (2019).Google ScholarGoogle Scholar
  14. ETSI ITS. 2020. Intelligent Transport System (ITS); Vehicular Communications; Basic Set of Applications; Specification of the Collective Perception Service (draft version). ETSI TR 103 324 V0.0.18 (May 2020).Google ScholarGoogle Scholar
  15. H. Khelifi et al. 2019. Named data networking in vehicular ad hoc networks: State-of-the-art and challenges. IEEE Communications Surveys & Tutorials 22, 1 (2019), 320--351.Google ScholarGoogle ScholarDigital LibraryDigital Library
  16. S. Kim et al. 2015. Multivehicle Cooperative Driving Using Cooperative Perception: Design and Experimental Validation. IEEE Trans. on Intelligent Transportation Systems 16, 2 (2015), 663--680.Google ScholarGoogle ScholarDigital LibraryDigital Library
  17. I. Ku et al. 2014. Towards software-defined VANET: Architecture and services. In 2014 IEEE MED-HOC-NET. 103--110.Google ScholarGoogle Scholar
  18. Xiaotong Li et al. 2020. Smart vehicular communication via 5G mmWaves. Computer Networks 172 (2020), 107173. Google ScholarGoogle ScholarCross RefCross Ref
  19. Z. Li, T. Yu, R. Fukatsu, G. K. Tran, and K. Sakaguchi. 2019. Proof-of-Concept of a SDN Based mmWave V2X Network for Safe Automated Driving. In 2019 IEEE GLOBECOM. 1--6.Google ScholarGoogle Scholar
  20. C. Liaskos et al. 2018. A New Wireless Communication Paradigm through Software-Controlled Metasurfaces. IEEE Communications Magazine 56, 9 (2018), 162--169.Google ScholarGoogle ScholarCross RefCross Ref
  21. A.L.R Madureira, F.R.C. Araújo, and L.N. Sampaio. 2020. On Supporting IoT Data Aggregation Through Programmable Data Planes. Computer Networks (2020), 107330.Google ScholarGoogle Scholar
  22. B.M. Masini, C.M. Silva, and A. Balador. 2020. The Use of Meta-Surfaces in Vehicular Networks. Journal of Sensor and Actuator Networks 9, 1 (2020), 15.Google ScholarGoogle ScholarCross RefCross Ref
  23. S. Mumtaz et al. 2017. Terahertz communication for vehicular networks. IEEE Trans. on Vehicular Technology 66, 7 (2017).Google ScholarGoogle ScholarCross RefCross Ref
  24. G. Naik, B. Choudhury, and J.M. Park. 2019. IEEE 802.11 bd & 5G NR V2X: Evolution of Radio Access Technologies for V2X Communications. IEEE Access (2019).Google ScholarGoogle Scholar
  25. A. Rauch, F. Klanner, and K. Dietmayer. 2011. Analysis of V2X communication parameters for the development of a fusion architecture for cooperative perception systems. In 2011 IEEE Intelligent Vehicles Symposium (IV). 685--690.Google ScholarGoogle Scholar
  26. A. Bazzi R. Verdone S. Mignardi, C. Buratti. 2019. Trajectories and resource management of flying base stations for C-V2X. Sensors 19, 4 (2019), 811.Google ScholarGoogle ScholarCross RefCross Ref
  27. A.E. Sallab, M. Abdou, E. Perot, and S. Yogamani. 2017. Deep reinforcement learning framework for autonomous driving. Electronic Imaging 2017, 19 (2017), 70--76.Google ScholarGoogle ScholarCross RefCross Ref
  28. W. Shi et al. 2018. Drone Assisted Vehicular Networks: Architecture, Challenges and Opportunities. IEEE Network 32, 3 (2018), 130--137.Google ScholarGoogle ScholarCross RefCross Ref
  29. R. Soua et al. 2018. Multi-Access Edge Computing for Vehicular Networks: A Position Paper. In IEEE Globecom Workshops 2018. 1--6.Google ScholarGoogle Scholar
  30. G. Thandavarayan, M. Sepulcre, and J. Gozalvez. 2018. Redundancy Mitigation in Cooperative Perception for Connected and Automated Vehicles. In 2020 IEEE 91st Vehicular Technology Conference. 1--5.Google ScholarGoogle Scholar
  31. G. Thandavarayan, M. Sepulcre, and J. Gozalvez. 2019. Analysis of Message Generation Rules for Collective Perception in Connected and Automated Driving. In 2019 IEEE Intelligent Vehicles Symposium (IV).Google ScholarGoogle Scholar
  32. T. Zugno et al. 2019. Towards standardization of millimeter wave vehicle-to-vehicle networks: Open challenges and performance evaluation. arXiv preprint arXiv:1910.00300 (2019).Google ScholarGoogle Scholar

Index Terms

  1. How to deal with data hungry V2X applications?

        Recommendations

        Comments

        Login options

        Check if you have access through your login credentials or your institution to get full access on this article.

        Sign in
        • Published in

          cover image ACM Conferences
          Mobihoc '20: Proceedings of the Twenty-First International Symposium on Theory, Algorithmic Foundations, and Protocol Design for Mobile Networks and Mobile Computing
          October 2020
          384 pages
          ISBN:9781450380157
          DOI:10.1145/3397166

          Copyright © 2020 ACM

          Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

          Publisher

          Association for Computing Machinery

          New York, NY, United States

          Publication History

          • Published: 11 October 2020

          Permissions

          Request permissions about this article.

          Request Permissions

          Check for updates

          Qualifiers

          • research-article

          Acceptance Rates

          Overall Acceptance Rate296of1,843submissions,16%

        PDF Format

        View or Download as a PDF file.

        PDF

        eReader

        View online with eReader.

        eReader