面向边缘节点的分布式可信系统研究
Research on Distributed Authentication of an Edge Computing System

DOI: 10.12677/CSA.2021.112040, PP. 400-409

Keywords: 5G，边缘计算，联盟链，可信认证机制
5G, Edge Computing, Consortium Blockchain, Trusted Authentication Mechanism

Full-Text   Cite this paper   Add to My Lib

Abstract:

随着物联网和5G的兴起，许多数据密集型的应用也随之发展了起来，如VR，高清视频。物联网终端之间的信息不互通且设备的数据安全性是一个亟待解决的问题。同时，针对移动物联网用户，基站往往会出现数据供应不及时，定位不准确等问题。由此，本文提出了一种基于区块链和边缘计算的分布式可信认证系统，旨在提高物联网终端节点的认证效率以及网络边缘数据的卸载效率。本系统由物理网络层，区块链边缘层和区块链网络层组成。通过区块链网络，设计优化了拜占庭容错共识算法，构建用于存储可信数据和日志的联盟链。此外，物联网边缘节点提供基于智能合约的域名解析和可信认证服务。同时，设计了一种非对称加密方法，以防止节点和终端之间通信时受到非法攻击。我们提出的认证机制是在通信和计算成本方面进行评估的。仿真结果表明，节点认证的时间被控制在一定时间内，本系统可广泛应用于基于不同形式的边缘计算网络，提供安全可靠的边缘缓存卸载服务。
With the development of the Internet of Things and 5G, many data-intensive applications have ap-peared, such as VR and high-definition video. The information between the terminals of the Internet of Things is not interoperable and the data security of the equipment is an urgent problem to be solved. Therefore, this paper proposes a distributed trusted authentication system based on block-chain and edge computing, which aims to improve the authentication efficiency of IoT terminal nodes and the offloading efficiency of network edge data. This system consists of a physical network layer, a blockchain edge layer and a blockchain network layer. Through the blockchain network, the Byzantine fault-tolerant consensus algorithm was designed and optimized, and a consortium chain for storing trusted data and logs was constructed. Simulation results show that the time for node authentication is controlled within a certain period of time. By deploying UAVs to assist edge node caching, this system can be widely used in edge computing networks based on different forms to provide safe and reliable edge caching offloading services.

References

[1]	Yang, X., Chen, Z., Li, K., Sun, Y., Liu, N., Xie, W. and Zhao, Y. (2018) Communication-Constrained Mobile Edge Computing Systems for Wireless Virtual Reality: Scheduling and Tradeoff. IEEE Access, 6, 16665-16677. https://doi.org/10.1109/ACCESS.2018.2817288
[2]	Shi, W., Cao, J., Zhang, Q., Li, Y. and Xu, L. (2016) Edge Computing: Vision and Challenges. IEEE Internet of Things Journal, 3, 637-646. https://doi.org/10.1109/JIOT.2016.2579198
[3]	Mao, Y., You, C., Zhang, J., Huang, K. and Letaief, K.B. (2017) A Survey on Mobile Edge Computing: The Communication Perspective. IEEE Communications Surveys & Tutorials, 19, 2322-2358. https://doi.org/10.1109/COMST.2017.2745201
[4]	Chen, M. and Hao, Y. (2018) Task Offloading for Mobile Edge Computing in Software Defined Ultra-Dense Network. IEEE Journal on Selected Areas in Communications, 36, 587-597. https://doi.org/10.1109/JSAC.2018.2815360
[5]	Mozaffari, M., Saad, W., Bennis, M., Nam, Y. and Debbah, M. (2019) A Tutorial on UAVs for Wireless Networks: Applications, Challenges, and Open Problems. IEEE Communications Surveys & Tutorials, 21, 2334-2360. https://doi.org/10.1109/COMST.2019.2902862
[6]	Li, B., Fei, Z. and Zhang, Y. (2019) UAV Communications for 5G and Beyond: Recent Advances and Future Trends. IEEE Internet of Things Journal, 6, 2241-2263. https://doi.org/10.1109/JIOT.2018.2887086
[7]	Chetlur Ravi, V.V. and Dhillon, H.S. (2016) Downlink Cover-age Probability in a Finite Network of Unmanned Aerial Vehicle (UAV) Base Stations. 2016 IEEE 17th International Workshop on Signal Processing Advances in Wireless Communications, Edinburgh, 3-6 July 2016, 1-5. https://doi.org/10.1109/SPAWC.2016.7536859
[8]	Cui, M., Zhang, G., Wu, Q. and Ng, D.W.K. (2018) Robust Trajectory and Transmit Power Design for Secure UAV Communications. IEEE Transactions on Vehicular Technology, 67, 9042-9046. https://doi.org/10.1109/TVT.2018.2849644
[9]	Islambouli, R. and Sharafeddine, S. (2019) Optimized 3D De-ployment of UAV-Mounted Cloudlets to Support Latency-Sensitive Services in IoT Networks. IEEE Access, 7, 172860-172870. https://doi.org/10.1109/ACCESS.2019.2956150
[10]	Hu, X., Wong, K., Yang, K. and Zheng, Z. (2019) UAV-Assisted Relaying and Edge Computing: Scheduling and Trajectory Optimization. IEEE Transactions on Wireless Communications, 18, 4738-4752. https://doi.org/10.1109/TWC.2019.2928539
[11]	Tang, Q., Chang, L., Yang, K., Wang, K.Z., Wang, J. and Shar-ma, P.K. (2020) Task Number Maximization of Floading Strategy Seamlessly Adapted to UAV Scenario. Computer Communications, 151, 19-30. https://doi.org/10.1016/j.comcom.2019.12.018
[12]	Zhou, F., Wu, Y., Hu, R.Q. and Qian, Y. (2018) Computation Rate Maximization in UAV-Enabled Wireless-Powered Mobile-Edge Computing Systems. IEEE Journal on Selected Areas in Communications, 36, 1927-1941. https://doi.org/10.1109/JSAC.2018.2864426
[13]	Lakiotakis, E., Sermpezis, P. and Dimitropou-los, X. (2019) Joint Optimization of UAV Placement and Caching under Battery Constraints in UAV-Aided Small-Cell Networks. ACM SIGCOMM2019 Workshop on Mobile AirGround Edge Computing, Systems, Net-Works, and Applications, Bei-jing, August 2019, 8-14. https://doi.org/10.1145/3341568.3342106
[14]	Khuller, S., Moss, A. and Naor, J.S. (1999) The Budgeted Maxi-mum Coverage Problem. Information Processing Letters, 70, 39-45. https://doi.org/10.1016/S0020-0190(99)00031-9
[15]	Krause, A. and Golovin, D. (2012) Submodular Function Maximization. In: Bordeaux, L., Hamadi, Y. and Kohli, P., Eds., Tractability: Practical Approaches to Hard Problems, Cambridge University Press, Cambridge, 71-104. https://doi.org/10.1017/CBO9781139177801.004
[16]	Caro, D.A. and Iovino, V. (2011) jPBC: Java Pairing Based Cryptography. 2011 IEEE Symposium on Computers and Communications, Kerkyra, 28 June-1 July 2011, 850-855. https://doi.org/10.1109/ISCC.2011.5983948

Full-Text