Connectivity probability analysis for freeway vehicle scenarios in vehicular networks

Abstract

The connectivity probability analysis of vehicular networks can be employed for providing theoretical guidance for both obtaining an accurate real-time traffic information and reducing hazardous traffic situations. Most previous studies focused on analyzing the connectivity probability of vehicular networks in physical (PHY) layer protocol. However, the effects of packet collision in media access control (MAC) layer on the connectivity probability of vehicular networks have been rarely studied, where MAC and PHY layers actually interact on each other. In this paper, some parameters are dynamically set and analyzed under consideration of the influence of MAC and PHY layers on the connectivity probability of vehicular networks. Numerical results are shown to be consistent with the proposed theoretical analysis.

This is a preview of subscription content, log in to check access.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6

References

  1. 1.

    Naboulsi, D., & Fiore, M. (2017). Characterizing the instantaneous connectivity of large-scale urban vehicular networks. IEEE Transactions on Mobile Computing, 16(5), 1272–1286.

    Article  Google Scholar 

  2. 2.

    Xiao, H., Zhang, Q., Ouyang, S., & Chronopoulos, A. T. (2020). Connectivity probability analysis for VANET freeway traffic using a cell transmission model. IEEE Systems Journal. https://doi.org/10.1109/JSYST.2020.3001938.

  3. 3.

    Muhammada, M., & Safdarb, G. A. (2018). Survey on existing authentication issues for cellular-assisted V2X communication. Vehicular Communications, 12, 50–60.

    Article  Google Scholar 

  4. 4.

    Jalooli, A., Song, M., & Wang, W. (2019). Message coverage maximization in infrastructure-based urban vehicular networks. Vehicular Communications, 16, 1–14.

    Article  Google Scholar 

  5. 5.

    Neelakantan, P., & Babu, A. (2012). Network connectivity probability of linear vehicular ad-hoc networks on two-way street. Commun. Net., 4(4), 332–341.

    Article  Google Scholar 

  6. 6.

    Jin, W., & Recker, W. (2010). An analytical model of multihop connectivity of inter-vehicle communication systems. IEEE Transactions on Wireless Communications, 9(1), 106–112.

    Article  Google Scholar 

  7. 7.

    Abdrabou, A., & Zhuang, W. (2010). Probabilistic delay control and road side unit placement for vehicular ad hoc networks with disrupted connectivity. IEEE Journal on Selected Areas in Communications, 29(1), 129–139.

    Article  Google Scholar 

  8. 8.

    Zhang, W., Chen, Y., Yang, Y., Wang, X., Zhang, Y., Hong, X., et al. (2012). Multi-hop connectivity probability in infrastructure-based vehicular networks. IEEE Journal on Selected Areas in Communications, 30(4), 740–747.

    Article  Google Scholar 

  9. 9.

    Alsharif, N., Shen, X., Alsharif, N., et al. (2017). iCAR-II: Infrastructure-based connectivity aware routing in vehicular networks. IEEE Transactions on Vehicular Technology, 66(5), 4231–4244.

    Article  Google Scholar 

  10. 10.

    Wang, Y., & Zheng, J. (2018). Connectivity analysis of a highway with one entry/exit and multiple roadside units. IEEE Transactions on Vehicular Technology, 67, 1–1.

  11. 11.

    Anshul, P., & Suneel, Y. (2020). Physical layer security in cooperative amplify and-forward relay networks over mixed Nakagami-m and double Nakagami-m fading channels: Performance evaluation and optimization. IET Communications, 14(1), 95–104.

    Article  Google Scholar 

  12. 12.

    Jayashree, T., Fadzilah, A. N., & Angela, D. (2020). V2V for vehicular safety applications. IEEE Transactions on Intelligent Transportation Systems, 21(6), 2571–2585.

    Google Scholar 

  13. 13.

    Yang, H., Yu, M., & Zeng, X. (2017). Link available time prediction based GPSR for vehicular ad hoc networks. In Proceedings of IEEE 14th International Conference on Network Sensor Control (ICNSC) (pp. 293–298).

  14. 14.

    Li, J., & Chen, M. (2020). A novel mobility-aware gradient forwarding algorithm for unmanned aerial vehicle ad hoc networks. Journal of Information Science and Engineering., 36(4), 851–864.

    Google Scholar 

  15. 15.

    Chen, J., Mao, G., Li, C., et al. (2017). Throughput of infrastructure-based cooperative vehicular networks. IEEE Transactions on Intelligent Transportation Systems, 18(11), 2964–2979.

    Article  Google Scholar 

  16. 16.

    Atallah, R., Khabbaz, M., & Assi, C. (2017). Multihop V2I communications: A feasibility study, modeling and performance analysis. IEEE Transactions on Vehicular Technology, 66(3), 2801–2810.

    Article  Google Scholar 

  17. 17.

    Anshul, P., & Suneel, Y. (2018). Physical layer security in cooperative AF relaying networks with direct links over mixed rayleigh and double-rayleigh fading channels. IEEE Transaction on Vehicular Technology, 67(11), 10615–10630.

    Article  Google Scholar 

  18. 18.

    Jameel, F., Haider, Faisal, M. A. A., & Butt, A. A. (2017). Performance analysis of VANETs under Rayleigh, Rician, Nakagami-m and Weibull fading. In International Conference on Communication, Computing and Digital Systems (pp. 127–132). IEEE.

  19. 19.

    Khan, Z., Fan, P., & Fand, S. (2017). On the connectivity of vehicular ad hoc network under various mobility scenarios. IEEE Access, 5, 22559–22565.

    Article  Google Scholar 

  20. 20.

    Rawat, D. B., Bista, B. B., Yan, G., & Olariu, S. (2014). Vehicle-to-vehicle connectivity and communication framework for vehicular ad-hoc networks. In International Conference on Complex, Intelligent and Software Intensive Systems (pp. 44–49). IEEE.

  21. 21.

    Yang, Q., Xing, S., Xia, W., & Shen, L. (2015). Modelling and performance analysis of dynamic contention window scheme for periodic broadcast in vehicular ad hoc networks. IET Communications, 9(11), 1347–1354.

    Article  Google Scholar 

  22. 22.

    Chung, J., Kim, M., Park, Y., Choi, M., Lee, S., & Oh, H. S. (2011). Time coordinated V2I communications and handover for WAVE networks. IEEE Journal on Selected Areas in Communications, 29(3), 545–558.

    Article  Google Scholar 

Download references

Acknowledgements

The authors acknowledge the support from the National Natural Science Foundation of China under Grants 61872406 and 61472094, Guangxi Natural Science Foundation under Grants 2014GXNSFGA118007, and Key research and development plan project of Zhejiang Province (No. 2018C01059).

Author information

Affiliations

Authors

Corresponding author

Correspondence to Hailin Xiao.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Xiao, H., Liu, X., Zhang, Q. et al. Connectivity probability analysis for freeway vehicle scenarios in vehicular networks. Wireless Netw (2020). https://doi.org/10.1007/s11276-020-02464-3

Download citation

Keywords

  • Connectivity probability
  • Vehicle-to-vehicle communications
  • Vehicle-to-infrastructure communications
  • Vehicular networks