Blockchain-Based Secure Communication of Intelligent Transportation Digital Twins System
Blockchain-Based Secure Communication of Intelligent Transportation Digital Twins System
复制标题
基于区块链的智能交通数字孪生系统安全通信
DOI：
10.1109/tits.2022.3183379
复制
发表时间：
2022-11
影响因子：
8.5
通讯作者：
Zhihan Lv
中科院分区：
文献类型：
--
作者：
Jun Liu;Lei Zhang;Chunlin Li;Jingpan Bai;Haibin Lv;Zhihan Lv
本工作旨在通过研究基于区块链（BC）的智能交通中车联网（IoV）的安全性，提高智能交通中车联网（IoV）节点的通信安全。车联网DTs模型是通过大数据与数字孪生（DTs）相结合构建的。然后，针对当前车联网通信安全问题，提出了一种基于不可变、可追踪BC数据的车联网系统安全通信架构。此外，Wasserstein基于距离的生成对抗网络（WaGAN）模型构建了车联网节点风险预测模型。由于WaGAN模型通过Wasserstein距离计算损失函数，因此模型的学习速度显着加快。经过十次迭代后，WaGAN 模型的损失率接近于零。车联网海量车载设备同时接入基站，造成网络通道拥堵。因此，提出了组认证和隐私保护(GAP)方案。随着认证过程中用户的增加，GAP方案的性能优于其他认证接入方案。综上所述，DT驱动的智能交通系统可以促进智能交通管理。此外，在车联网中引入BC可以提高门禁控制的准确性和响应效率。本文报道的研究对于提高车联网信息共享的安全性具有重要价值。
The present work aims to improve the communication security of Internet of Vehicles (IoV) nodes in intelligent transportation through studying the safety of IoV in smart transportation based on Blockchain (BC). An IoV DTs model is built by combining big data with Digital Twins (DTs). Then, regarding the current IoV communication security issues, a secure communication architecture for the IoV system is proposed based on the immutable and trackable BC data. Besides, Wasserstein Distance Based Generative Adversarial Network (WaGAN) model constructs the IoV node risk forecast model. Because the WaGAN model calculates the loss function through Wasserstein distance, the learning rate of the model accelerates remarkably. After ten iterations, the loss rate of the WaGAN model is close to zero. Massive in-vehicle devices in IoV are connected simultaneously to the base station, causing network channel congestion. Therefore, a Group Authentication and Privacy-preserving (GAP) scheme is put forward. As users increase during authentication, the GAP scheme performs better than other authentication access schemes. In summary, the Intelligent Transportation System driven by DTs can promote intelligent transportation management. Besides, introducing BC into IoV can improve access control’s accuracy and response efficiency. The research reported here has significant value for improving the security of the information sharing of the IoV.
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DOI：
10.3390/s21020515
发表时间：
2021-01-13
期刊：
Sensors (Basel, Switzerland)
影响因子：
--
作者：
La Manna M;Treccozzi L;Perazzo P;Saponara S;Dini G
通讯作者：
Dini G
DOI：
10.18178/ijmlc.2018.8.3.702
发表时间：
2018-06
期刊：
International Journal of Machine Learning and Computing
影响因子：
--
作者：
Koorosh Gharehbaghi;K. Farnes
通讯作者：
Koorosh Gharehbaghi;K. Farnes
影响因子：
3.8
作者：
Ajayi, Anuoluwapo;Oyedele, Lukumon;Akanbi, Lukman
通讯作者：
Akanbi, Lukman
影响因子：
3.9
作者：
Xiaoliang Wang;Pengjie Zeng;Nicholas J. Patterson;Frank Jiang;R. Doss
通讯作者：
Xiaoliang Wang;Pengjie Zeng;Nicholas J. Patterson;Frank Jiang;R. Doss
影响因子：
3.9
作者：
Chang, Wan-Jung;Chen, Liang-Bi;Su, Ke-Yu
通讯作者：
Su, Ke-Yu