Visual-MIMO for Software-Defined Vehicular Networks

  • Authors

    • Tae Ho Kwon
    • Jai Eun Kim
    • Ki Soo An
    • Rappy Saha
    • Ki Doo Kim
    2018-09-15
    https://doi.org/10.14419/ijet.v7i4.4.19596
  • software defined network (SDN), visual-MIMO, generalized color modulation (GCM), vehicular networking, mobile optical networking
  • Abstract

    The paradigm of software-defined network (SDN) is being applied to vehicle scenarios in order to eliminate this heterogeneity of vehicular network infrastructure and to manage packet flow in an application- and user-centrically flexible and efficient manner. However, owing to the random mobility of vehicles and the unpredictable road communication environment, efficient vehicle-based SDN development needs further research. In this study, we propose the concept of a sub-control plane for supporting and backing up, at the data plane level, various functions of the control plane, which plays a key role in SDN. The sub-control plane can be intuitively understood through the image processing techniques used in color-independent visual-MIMO (multiple input multiple output) networking, and the function of the control plane can be backed up through various vehicle-based recognition and tracking algorithms under the situation of disconnection between the data plane and the control plane. The proposed sub-control plane is expected to facilitate efficient management of the software-defined vehicular network (SDVN) and improve vehicular communication performance and service quality.

     

     

  • References

    1. [1] I. Ku et al., Towards Software-Defined VANET: Architecture and Services, Proc. Mediterranean Ad Hoc Networking Wksp., 2014.

      [2] Z. He, J. Cao, X. Liu, SDVN: Enabling Rapid Network Innovation for Heterogeneous Vehicular Communication, IEEE Network, 30 (2016), 10-15.

      [3] J.-E. Kim, J.-W. Kim, Y. Park, and K.-D. Kim, Color-Space-Based Visual-MIMO for V2X Communication, Sensors, 16 (2016).

      [4] S. Sivaraman and M. M. Trivedi, Looking at vehicles on the road: A survey of vision-based vehicle detection tracking and behavior analysis, IEEE Trans. Intell. Transp. Syst., 14 (2013), 1773-1795.

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  • How to Cite

    Ho Kwon, T., Eun Kim, J., Soo An, K., Saha, R., & Doo Kim, K. (2018). Visual-MIMO for Software-Defined Vehicular Networks. International Journal of Engineering & Technology, 7(4.4), 13-14. https://doi.org/10.14419/ijet.v7i4.4.19596

    Received date: 2018-09-12

    Accepted date: 2018-09-12

    Published date: 2018-09-15