Key Technologies of MEC Towards 5G-Enabled Vehicular Networks

Conference paper
Part of the Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering book series (LNICST, volume 234)

Abstract

Mobile edge computing (MEC) can satisfy the communication requirements of ultra-high reliability and ultra-low latency in 5G-enabled vehicular networks, since it provides Internet service environment and cloud computing capability for wireless access network. In this paper, the architecture and characteristics of MEC for unmanned driving are explored. Meanwhile, the key technologies of MEC are discussed. With the assist of clustering, we propose the scheme of mobile vehicle cloud (MVC)-aided communication, and examine the network performance including computing resource allocation by MEC and link performance. The numerical results show that the network performance is improved effectively.

Keywords

5G vehicular networks MEC MVC-aided communication Clustering Network performance 

Notes

Acknowledgments

This work was supported in part by the National Natural Science Foundation of China (61471031, 61661021), the State Key Laboratory of Rail Traffic Control and Safety (Contract No. RCS2017K009), the Key Technology Research and Development Program of Jiangxi Province under Grant No. 20171BBE50057, the Open Research Fund of National Mobile Communications Research Laboratory, Southeast University (No. 2017D14), Science and technology project of Jiangxi Provincial Transport Bureau (No. 2016D0037), Training Plan for the Main Subject of Academic Leaders of Jiangxi Province (No. 20172BCB22016), and Natural Science Foundation of Guangdong Province under Grant No. 2015A030313844.

References

  1. 1.
    Hu, Y.C., Patel, M., Sabella, D.: Mobile edge computing: a key technology towards 5G. ETSI White Paper 11(11), 1–16 (2015)Google Scholar
  2. 2.
    Zhao, J.H., Chen, Y., Huang, D.C.: Study on key technology of VANET sin terminal management cloud model. Telecommun. Sci. 32(8), 2–9 (2016)Google Scholar
  3. 3.
    Zhang, K., Mao, Y., Leng, S., Maharjan, S., Zhang, Y.: Optimal delay constrained offloading for vehicular edge computing networks. In: IEEE International Conference on Communications (ICC), pp. 1–6. IEEE Press, Paris (2017)Google Scholar
  4. 4.
    Zhang, K., Mao, Y., Leng, S.: Predictive offloading in cloud-driven vehicles: using mobile-edge computing for a promising network paradigm. IEEE Veh. Technol. Mag. 12, 36–44 (2017)CrossRefGoogle Scholar
  5. 5.
    Hou, X., Li, Y., Chen, M., Wu, D., Jin, D., Chen, S.: Vehicular fog computing: a viewpoint of vehicles as the infrastructures. IEEE Trans. Veh. Technol. 65, 3860–3873 (2016)CrossRefGoogle Scholar
  6. 6.
    Campolo, C., Molinaro, A., Araniti, G., Berthet, A.O.: Better platooning control toward autonomous driving: an LTE device-to-device communications strategy that meets ultralow latency requirements. IEEE Veh. Technol. Mag. 12, 30–38 (2017)CrossRefGoogle Scholar
  7. 7.
    Zhao, J.H., Chen, Y., Gong, Y.: Study of connectivity probability based on cluster in vehicular ad hoc networks. In: 8th International Conference on Wireless Communications & Signal Processing (WCSP), pp. 1–5. IEEE Press, Yangzhou (2016)Google Scholar
  8. 8.
    Reputation-Based Approach for Computation Offloading in Vehicular Edge Computing. http://www.arocmag.com/article/02-2018-09-002.html

Copyright information

© ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering 2018

Authors and Affiliations

  • Xiaoting Ma
    • 1
    • 2
  • Junhui Zhao
    • 1
    • 2
  • Yi Gong
    • 3
  • Yijie Wang
    • 1
  1. 1.School of Information EngineeringEast China Jiaotong UniversityNanchangChina
  2. 2.School of Electronic and Information EngineeringBeijing Jiaotong UniversityBeijingChina
  3. 3.Department of Electrical and Electronic EngineeringSouthern University of Science and TechnologyShenzhenChina

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