A journal of IEEE and CAA , publishes high-quality papers in English on original theoretical/experimental research and development in all areas of automation
Volume 7 Issue 2
Mar.  2020

IEEE/CAA Journal of Automatica Sinica

  • JCR Impact Factor: 15.3, Top 1 (SCI Q1)
    CiteScore: 23.5, Top 2% (Q1)
    Google Scholar h5-index: 77, TOP 5
Turn off MathJax
Article Contents
Xumin Huang, Dongdong Ye, Rong Yu and Lei Shu, "Securing Parked Vehicle Assisted Fog Computing With Blockchain and Optimal Smart Contract Design," IEEE/CAA J. Autom. Sinica, vol. 7, no. 2, pp. 426-441, Mar. 2020. doi: 10.1109/JAS.2020.1003039
Citation: Xumin Huang, Dongdong Ye, Rong Yu and Lei Shu, "Securing Parked Vehicle Assisted Fog Computing With Blockchain and Optimal Smart Contract Design," IEEE/CAA J. Autom. Sinica, vol. 7, no. 2, pp. 426-441, Mar. 2020. doi: 10.1109/JAS.2020.1003039

Securing Parked Vehicle Assisted Fog Computing With Blockchain and Optimal Smart Contract Design

doi: 10.1109/JAS.2020.1003039
Funds:  This work was supported in part by the National Natural Science Foundation of China (61971148), the Science and Technology Program of Guangdong Province (2015B010129001), the Natural Science Foundation of Guangxi Province (2018GXNSFDA281013), the Foundation for Science and Technology Project of Guilin City (20190214-3), and the Key Science and Technology Project of Guangxi (AA18242021)
More Information
  • Vehicular fog computing (VFC) has been envisioned as an important application of fog computing in vehicular networks. Parked vehicles with embedded computation resources could be exploited as a supplement for VFC. They cooperate with fog servers to process offloading requests at the vehicular network edge, leading to a new paradigm called parked vehicle assisted fog computing (PVFC). However, each coin has two sides. There is a follow-up challenging issue in the distributed and trustless computing environment. The centralized computation offloading without tamper-proof audit causes security threats. It could not guard against false-reporting, free-riding behaviors, spoofing attacks and repudiation attacks. Thus, we leverage the blockchain technology to achieve decentralized PVFC. Request posting, workload undertaking, task evaluation and reward assignment are organized and validated automatically through smart contract executions. Network activities in computation offloading become transparent, verifiable and traceable to eliminate security risks. To this end, we introduce network entities and design interactive smart contract operations across them. The optimal smart contract design problem is formulated and solved within the Stackelberg game framework to minimize the total payments for users. Security analysis and extensive numerical results are provided to demonstrate that our scheme has high security and efficiency guarantee.

     

  • loading
  • [1]
    X. S. Hou, Y. Li, M. Chen, D. Wu, D. P. Jin, and S. Chen, “Vehicular fog computing: a viewpoint of vehicles as the infrastructures,” IEEE Trans. Vehicular Technology, vol. 65, pp. 3860–3873, Jun. 2016. doi: 10.1109/TVT.2016.2532863
    [2]
    Q. Fan and N. Ansari, “On cost aware cloudlet placement for mobile edge computing,” IEEE/CAA J. Autom. Sinica, vol. 6, no. 4, pp. 926–937, 2019. doi: 10.1109/JAS.2019.1911564
    [3]
    Z. L. Ning, J. Huang, and X. J. Wang, “Vehicular fog computing: enabling real-time traffic management for smart cities,” IEEE Wireless Communications, vol. 26, no. 1, pp. 87–93, Feb. 2019.
    [4]
    Y. Zhang, C.-Y. Wang, and H.-Y. Wei, “Parking reservation auction for parked vehicle assistance in vehicular fog computing,” IEEE Trans. Vehicular Technology, vol. 68, no. 4, pp. 3126–3139, Apr. 2019. doi: 10.1109/TVT.2019.2899887
    [5]
    A. Lei, H. Cruickshank, Y. Cao, P. Asuquo, C. P. A. Ogah, and Z. L. Sun, “Blockchain-based dynamic key management for heterogeneous intelligent transportation systems,” IEEE Internet of Things J., vol. 4, pp. 1832–1843, Dec. 2017. doi: 10.1109/JIOT.2017.2740569
    [6]
    L. Li, J. Q. Liu, L. C. Cheng, S. Qiu, W. Wang, X. L. Zhang, and Z. H. Zhang, “Creditcoin: a privacy-preserving blockchain-based incentive announcement network for communications of smart vehicles,” IEEE Trans. Intelligent Transportation Systems, vol. 19, pp. 2204–2220, Jul. 2018. doi: 10.1109/TITS.2017.2777990
    [7]
    J. W. Kang, R. Yu, X. M. Huang, M. Q. Wu, S. Maharjan, S. L. Xie, and Y. Zhang, “Blockchain for secure and efficient data sharing in vehicular edge computing and networks,” IEEE Internet of Things J., vol. 6, pp. 4660–4670, Jun. 2019. doi: 10.1109/JIOT.2018.2875542
    [8]
    X. Y. Kui, Y. Sun, S. G. Zhang, and Y. Li, “Characterizing the capability of vehicular fog computing in large-scale urban environment,” Mobile Networks and Applications, vol. 23, pp. 1050–1067, Aug. 2018. doi: 10.1007/s11036-017-0969-8
    [9]
    C. Huang, R. X. Lu, and K.-K. R. Choo, “Vehicular fog computing: architecture, use case, and security and forensic challenges,” IEEE Communications Magazine, vol. 55, no. 11, pp. 105–111, Nov. 2017. doi: 10.1109/MCOM.2017.1700322
    [10]
    Z. L. Ning, P. R. Dong, X. J. Wang, J. J. Rodrigues, and F. Xia, “Deep reinforcement learning for vehicular edge computing: an intelligent offloading system,” ACM Trans. Intelligent Systems and Technology, vol. 10, no. 6, pp. 60, May 2019.
    [11]
    X. J. Wang, Z. L. Ning, and L. Wang, “Offloading in internet of vehicles: a fog-enabled real-time traffic management system,” IEEE Trans. Industrial Informatics, vol. 14, pp. 4568–4578, Oct. 2018. doi: 10.1109/TII.2018.2816590
    [12]
    C. Zhu, J. Tao, G. Pastor, Y. Xiao, Y. S. Ji, Q. Zhou, Y. Li, and A. Ylä-Jääski, “Folo: latency and quality optimized task allocation in vehicular fog computing,” IEEE Internet of Things J., vol. 6, no. 3, pp. 4150–4161, Jun. 2019. doi: 10.1109/JIOT.2018.2875520
    [13]
    X. M. Huang, R. Yu, J. Q. Liu, and L. Shu, “Parked vehicle edge computing: exploiting opportunistic resources for distributed mobile applications,” IEEE Access, vol. 6, pp. 66649–66663, 2018. doi: 10.1109/ACCESS.2018.2879578
    [14]
    Y. T. Wang, M. Sheng, X. J. Wang, L. Wang, W. J. Han, Y. Zhang, and Y. Shi, “Energy-optimal partial computation offloading using dynamic voltage scaling,” in Proc. IEEE Int. Conf. Communication Workshop (ICCW), pp. 2695–2700, Jun. 2015.
    [15]
    Z. J. Lu, W. C. Liu, Q. Wang, G. Qu, and Z. L. Liu, “A privacy-preserving trust model based on blockchain for vanets,” IEEE Access,, vol. 6, pp. 45655–45664, 2018. doi: 10.1109/ACCESS.2018.2864189
    [16]
    Z. Yang, K. Yang, L. Lei, K. Zheng, and V. C. M. Leung, “Blockchainbased decentralized trust management in vehicular networks,” IEEE Internet of Things J., vol. 6, pp. 1495–1505, Apr. 2019. doi: 10.1109/JIOT.2018.2836144
    [17]
    W. C. Xu, H. B. Zhou, N. Cheng, F. Lyu, W. S. Shi, J. Y. Chen, and X. M. Shen, “Internet of vehicles in big data era,” IEEE/CAA J. Autom. Sinica, vol. 5, no. 1, pp. 19–35, Jan. 2017.
    [18]
    Z. H. Xiong, Y. Zhang, D. Niyato, P. Wang, and Z. Han, “When mobile blockchain meets edge computing,” IEEE Communications Magazine, vol. 56, pp. 33–39, Aug. 2018.
    [19]
    G. Wood, “Ethereum: a secure decentralised generalised transaction ledger,” Ethereum Project Yellow Paper, vol. 151, pp. 1–32, 2014.
    [20]
    S. Y. Zhao, V. Lo, and C. G. Dickey, “Result verification and trust-based scheduling in peer-to-peer grids,” in Proc. 15th IEEE Int. Conf. Peer-to-Peer Computing (P2P’05), pp. 31–38, IEEE, Jan. 2005.
    [21]
    M. Walfish and A. J. Blumberg, “Verifying computations without reexecuting them,” Communications of the ACM, vol. 58, no. 2, pp. 74–84, 2015. doi: 10.1145/2641562
    [22]
    O. Kupyn, V. Budzan, M. Mykhailych, D. Mishkin, and J. Matas, “Deblurgan: blind motion deblurring using conditional adversarial networks,” in Proc. IEEE Conf. Computer Vision and Pattern Recognition, pp. 8183–8192, 2018.
    [23]
    M. Chen and Y. X. Hao, “Task offloading for mobile edge computing in software defined ultra-dense network,” IEEE J. Selected Areas in Communications, vol. 36, no. 3, pp. 587–597, Mar. 2018. doi: 10.1109/JSAC.2018.2815360
    [24]
    O. Munoz, A. Pascual-Iserte, and J. Vidal, “Optimization of radio and computational resources for energy efficiency in latency-constrained application offloading,” IEEE Trans. Vehicular Technology, vol. 64, pp. 4738–4755, Oct. 2015. doi: 10.1109/TVT.2014.2372852
    [25]
    L. Rao, X. Liu, M. D. Ilic, and J. Liu, “Distributed coordination of internet data centers under multiregional electricity markets,” Proc. the IEEE, vol. 100, pp. 269–282, Jan. 2012. doi: 10.1109/JPROC.2011.2161236
    [26]
    X. M. Wang, X. M. Chen, W. W. Wu, N. An, and L. S. Wang, “Cooperative application execution in mobile cloud computing: a stackelberg game approach,” IEEE Communications Letters, vol. 20, pp. 946–949, May 2016. doi: 10.1109/LCOMM.2015.2506580
    [27]
    W. W. Zhang, Y. G. Wen, K. Guan, D. Kilper, H. Y. Luo, and D. O. Wu, “Energyoptimal mobile cloud computing under stochastic wireless channel,” IEEE Trans. Wireless Communications, vol. 12, pp. 4569–4581, Sep. 2013. doi: 10.1109/TWC.2013.072513.121842
    [28]
    X. J. Liu, W. B. Wang, D. Niyato, N. Zhao, and P. Wang, “Evolutionary game for mining pool selection in blockchain networks,” IEEE Wireless Communications Letters, vol. 7, pp. 760–763, Oct. 2018. doi: 10.1109/LWC.2018.2820009
    [29]
    D. J. Yang, G. L. Xue, X. Fang, and J. Tang, “Incentive mechanisms for crowdsensing: Crowdsourcing with smartphones,” IEEE/ACM Trans. Netw., vol. 24, pp. 1732–1744, Jun. 2016. doi: 10.1109/TNET.2015.2421897
    [30]
    J. L. Pan, J. Y. Wang, A. Hester, I. Alqerm, Y. N. Liu, and Y. Zhao, “Edgechain: an edge-iot framework and prototype based on blockchain and smart contracts,” IEEE Internet of Things J., vol. 6, pp. 4719–4732, Jun. 2019. doi: 10.1109/JIOT.2018.2878154
    [31]
    A. Bazzi, B. M. Masini, A. Zanella, and I. Thibault, “On the performance of IEEE 802.11p and LTE-V2V for the cooperative awareness of connected vehicles,” IEEE Trans. Vehicular Technology, vol. 66, pp. 10419–10432, Nov. 2017. doi: 10.1109/TVT.2017.2750803
    [32]
    ACT Government Open Data Portal dataACT. [Online]. Available: https://www.data.act.gov.au/Transport/SmartParking-History/grth-myzs, 2017.
    [33]
    X. L. He, Z. Y. Ren, C. H. Shi, and J. Fang, “A novel load balancing strategy of software-defined cloud/fog networking in the internet of vehicles,” China Communications, vol. 13, pp. 140–149, Nov. 2016. doi: 10.1109/CC.2016.7405730
    [34]
    G. Xiong, F. H. Zhu, X. W. Liu, X. S. Dong, W. L. Huang, S. H. Chen, and K. Zhao, “Cyber-physical-social system in intelligent transportation,” IEEE/CAA J. Autom. Sinica, vol. 2, no. 3, pp. 320–333, 2015. doi: 10.1109/JAS.2015.7152667

Catalog

    通讯作者: 陈斌, bchen63@163.com
    • 1. 

      沈阳化工大学材料科学与工程学院 沈阳 110142

    1. 本站搜索
    2. 百度学术搜索
    3. 万方数据库搜索
    4. CNKI搜索

    Figures(7)  / Tables(2)

    Article Metrics

    Article views (1814) PDF downloads(123) Cited by()

    Highlights

    • Parked Vehicle assisted Fog Computing (PVFC) is introduced to exploit numerous parked vehicles with embedded computation resources as a great supplement for Vehicular Fog Computing (VFC).
    • PVFChain with an optimal smart contract design is further proposed to conduct offloading services in a totally decentralized way. Finally, the scheme improves the network security and efficiency by leveraging the blockchain technology.
    • Request posting, workload undertaking, task evaluation and reward assignment are organized and validated automatically through smart contract executions. The optimal smart contract design problem is also formulated and solved within the Stackelberg game framework to minimize service fee for users and enrich user satisfaction in computation offloading.

    /

    DownLoad:  Full-Size Img  PowerPoint
    Return
    Return