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Consortium Blockchain-Based Secure Software Defined Vehicular Network

  • Ning Zhao
  • Hao WuEmail author
  • Xiaonan Zhao
Article
  • 57 Downloads

Abstract

The vehicular ad hoc network (VANET) is a promising technology that can provide Internet access services for vehicles. With the development of VANET, tremendous intelligent vehicles will emerge mass and different communication requirements. Software-defined networking (SDN) is regarded as a potential technology to enhance network performance. In recent years, a new networking paradigm called software defined vehicular networks (SDVN) has been proposed. Nevertheless, the security issues still need to be considered for SDVN, because malicious vehicles can put forward fake requirements on the control plane of SDVN, which deteriorates the network performance in a certain degree. In this paper, we associate resources allocation problem with trust value of vehicles for the first time. The trust value of vehicles can be obtained through trust management system. Considering that there are many defects in the state-in-art trust management schemes, in this paper, a decentralized trust management architecture is designed which constitutes of three layers based on consortium blockchain. A joint proof-of-stake and modified practical Byzantine fault tolerance (PoS-mPBFT) algorithm is proposed to the shorten the confirmation time, which is deployed on RSUs. Different from previous researches that focus on designing methods to evaluate trust value, we use prediction model to estimate trust value of vehicles in the next period. After calculating trust value of vehicles, it assigns more resources to those high credibility vehicles when SDN services are provided. Meanwhile, to increase the efficiency of resource allocation, we convert the multiple-path mapping problem of the virtual network into the multi-commodity flow problem, which is solved by a heuristic algorithm. The simulation results indicate that the proposed trust management architecture and heuristic algorithm could provide better safety in SDVN and shorten consensus time, meanwhile effectively abstract underlying resources to enhance network load balance and capacity.

Keywords

SDN Vehicular network Trust management Consortium blockchain 

Notes

References

  1. 1.
    Chen C, Wang Z, Guo B (2016) The road to the chinese smart city: progress, challenges, and future directions. IT PROF 18(1):14–17CrossRefGoogle Scholar
  2. 2.
    Nunes BA, Mendonca M, Nguyen X, Obraczka K, Turletti T (2014) A survey of software-defined networking: past, present, and future of programmable networks. IEEE Commun Surveys Tuts 16(3):1617–1634CrossRefGoogle Scholar
  3. 3.
    He Z, Cao J, Liu X (2016) SDVN: Enabling rapid network innovation for heterogeneous vehicular communication. IEEE Netw 30(4):10–15CrossRefGoogle Scholar
  4. 4.
    Ozcevik ME, Canberk B, Duong TQ (2017) End to end delay modeling of heterogeneous traffic flows in software defined 5G networks. Ad Hoc Netw 60:26–39CrossRefGoogle Scholar
  5. 5.
    Li H, Dong M, Ota K (2016) Control plane optimization in software-defined vehicular ad hoc networks. IEEE Trans Veh Technol 65(10):7895–7904CrossRefGoogle Scholar
  6. 6.
    Yaqoon Y, Ahmad I, Ahmed E, Gani A, Imran M, Guizani N (2017) Overcoming the key challenges to establishing vehicular communication: is SDN the answer? IEEE Commun Mag 55(7):128–134CrossRefGoogle Scholar
  7. 7.
    Mahmoud ME, Shen X (2011) An integrated stimulation and punishment mechanism for thwarting packet dropping attack in multihop wireless networks. IEEE Trans Veh Technol 60(8):3947–3962CrossRefGoogle Scholar
  8. 8.
    Li Z, Chi G, Tricia C (2014) On joint privacy and reputation assurance for vehicular ad hoc networks. IEEE Trans Mobile Comput 13(10):2334–2344CrossRefGoogle Scholar
  9. 9.
    Huang X, Yu R, Kang J, Zhang Y (2017) Distributed reputation management for secure and efficient vehicular edge computing and networks. IEEE Access 5:25408–25420CrossRefGoogle Scholar
  10. 10.
    Zheng Q, Zheng K, Zhang H, Leung VC (2016) Delay-optimal virtualized radio resource scheduling in software-defined vehicular networks via stochastic learning. IEEE Trans Veh Technol 65(10):7857–7867CrossRefGoogle Scholar
  11. 11.
    Zhu Y, Ammmar M (2006) Algorithms for assigning substrate network resources to virtual network components. In: 25th IEEE international conference on computer communications (INFOCOM), pp 1–12Google Scholar
  12. 12.
    Lai C, Zhang K, Cheng N, Li H, Shen X (2017) SIRC: A secure incentive scheme for reliable cooperative downloading in highway VANETs. IEEE Trans Intell Transport Syst 18(6):1559–1574Google Scholar
  13. 13.
    Li Q, Malip A, Martin KM, Ng S, Zhang J (2012) A reputation-based announcement scheme for VANETs. IEEE Trans Veh Technol 61(9):4095–4108CrossRefGoogle Scholar
  14. 14.
    Raya M, Papadimitratos P, Gligor VD, Hubaux J (2008) On data-centric trust establishment in ephemeral ad hoc networks. In: IEEE 27th conference on computer communications (INFOCOM), pp 1238–1246Google Scholar
  15. 15.
    Yang Z, Yang K, Lei L, Zheng K, Leung VC (2018) Blockchain-based decentralized trust management in vehicular networks. IEEE Internet of Things Journal early accessGoogle Scholar
  16. 16.
    Secinti G, Canberk B, Duong TQ, Shu L (2017) Software defined architecture for VANET: a testbed implementation with wireless access management. IEEE Commun Mag 55(7):135–141CrossRefGoogle Scholar
  17. 17.
    Muzio JC, Rosenerg IC (1986) Introduction—multiple-valued logic. IEEE Trans Comput C-35(2):97–98CrossRefGoogle Scholar
  18. 18.
  19. 19.
    Castro M, Liskov B (1999) Practical byzantine fault tolerance. In: Third symposium on operating systems design and implementation (OSDI)Google Scholar
  20. 20.
    Gonzlez A, Barra E, Beghelli A, Leiva A (2015) A sub-graph mapping-based algorithm for virtual network allocation over flexible grid networks. In: IEEE 17th International Conference on Transparent Optical Networks (ICTON), pp 1–4Google Scholar
  21. 21.
    Jiang M, Wang B, Wu M (2011) Research on network virtualization and virtual network mapping algorithm. Chin J Electron 39(6):1315–1320Google Scholar
  22. 22.
    Martin-Vega FJ, Aguayo-Torres MC, Gomez G, Entrambasaguas JT, Duong TQ (2018) Key technologies, modeling approaches, and challenges for millimeter-wave vehicular communications. IEEE Communication Magazine 56(10):28 C 35CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  1. 1.State Key Laboratory of Rail Traffic Control and SafetyBeijing Jiaotong UniversityBeijingChina
  2. 2.School of Electronic and Information EngineeringBeijing Jiaotong UniversityBeijingChina

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