Digital Twin Enabled Remote Data Sharing for Internet of Vehicles: System and Incentive Design

被引:17
作者
Tan, Chenchen [1 ]
Li, Xinghao [1 ]
Gao, Longxiang [2 ,3 ]
Luan, Tom H. [3 ,4 ]
Qu, Youyang [2 ]
Xiang, Yong [1 ]
Lu, Rongxing [5 ]
机构
[1] Deakin Univ, Sch Informat Technol, Melbourne, Vic 3125, Australia
[2] Qilu Univ Technol, Shandong Acad Sci, Jinan 250316, Peoples R China
[3] Natl Supercomp Ctr Jinan, Shandong Comp Sci Ctr, Jinan 250101, Peoples R China
[4] Xidian Univ, Sch Cyber Engn, Xian 710071, Peoples R China
[5] Univ New Brunswick, Fac Comp Sci, Fredericton E3B 5A3, NB, Canada
基金
中国国家自然科学基金;
关键词
Data sharing; digital twins; dynamic contract theory; federated learning; Internet of Vehicles; transfer learning; INTRUSION DETECTION; BLOCKCHAIN; MECHANISM; SCHEME;
D O I
10.1109/TVT.2023.3275591
中图分类号
TM [电工技术]; TN [电子技术、通信技术];
学科分类号
0808 ; 0809 ;
摘要
With the boom in advanced intelligent vehicles, the amount of data associated with the Internet of Vehicles (IoVs) grows exponentially. Sharing road data among vehicles effectively can greatly improve their driving efficiency and road experience. This, however, is hindered by security concerns, bandwidth limitations, and mobility and communication distance between vehicles. In this article, we propose a citywide data sharing platform among vehicles based on the digital twin. Specifically, we propose a digital twin based vehicular platform on the cloud to facilitate the effective data sharing of vehicles. A digital twin is a digital representation of a vehicle on the cloud that synchronises data with the vehicle in real time. Therefore, data sharing among vehicles can be accomplished by their digital twins on the cloud without any physical limitations. Considering the data privacy of vehicles, a framework that combines federated learning and transfer learning is applied which can realise personalised data sharing among vehicles without disclosing their data privacy. An incentive mechanism based on the game-theoretic approach is devised to combat the mutual distrust between vehicles and encourage their contribution to data sharing. By sewing the above mechanisms, the proposed digital twin enabled data sharing platform can effectively address privacy, bandwidth limitation and incentive issues. Using extensive trace-driven simulations, we demonstrate the effectiveness and efficiency of the proposed system.
引用
收藏
页码:13474 / 13489
页数:16
相关论文
共 35 条
[1]   Game Theory-Based Control Strategy For Trajectory Following of Four-Wheel Independently Actuated Autonomous Vehicles [J].
An, Quan ;
Cheng, Shuo ;
Li, Chenfeng ;
Li, Liang ;
Peng, Haonan .
IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY, 2021, 70 (03) :2196-2208
[2]   Predictive Battery Health Management With Transfer Learning and Online Model Correction [J].
Che, Yunhong ;
Deng, Zhongwei ;
Lin, Xianke ;
Hu, Lin ;
Hu, Xiaosong .
IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY, 2021, 70 (02) :1269-1277
[3]   FedSVRG Based Communication Efficient Scheme for Federated Learning in MEC Networks [J].
Chen, Dawei ;
Hong, Choong Seon ;
Zha, Yiyong ;
Zhang, Yunfei ;
Liu, Xin ;
Han, Zhu .
IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY, 2021, 70 (07) :7300-7304
[4]   FedHealth: A Federated Transfer Learning Framework for Wearable Healthcare [J].
Chen, Yiqiang ;
Qin, Xin ;
Wang, Jindong ;
Yu, Chaohui ;
Gao, Wen .
IEEE INTELLIGENT SYSTEMS, 2020, 35 (04) :83-93
[5]   Secure and Efficient Data Sharing Among Vehicles Based on Consortium Blockchain [J].
Cui, Jie ;
Ouyang, Fenqiang ;
Ying, Zuobin ;
Wei, Lu ;
Zhong, Hong .
IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS, 2022, 23 (07) :8857-8867
[6]   FAIR: Quality-Aware Federated Learning with Precise User Incentive and Model Aggregation [J].
Deng, Yongheng ;
Lyu, Feng ;
Ren, Ju ;
Chen, Yi-Chao ;
Yang, Peng ;
Zhou, Yuezhi ;
Zhang, Yaoxue .
IEEE CONFERENCE ON COMPUTER COMMUNICATIONS (IEEE INFOCOM 2021), 2021,
[7]  
Ding NN, 2020, 2020 18TH INTERNATIONAL SYMPOSIUM ON MODELING AND OPTIMIZATION IN MOBILE, AD HOC, AND WIRELESS NETWORKS (WIOPT)
[8]   Automatic Modulation Classification Based on Decentralized Learning and Ensemble Learning [J].
Fu, Xue ;
Gui, Guan ;
Wang, Yu ;
Gacanin, Haris ;
Adachi, Fumiyuki .
IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY, 2022, 71 (07) :7942-7946
[9]   Security and Privacy in Vehicular Digital Twin Networks: Challenges and Solutions [J].
He, Chao ;
Luan, Tom H. ;
Lu, Rongxing ;
Su, Zhou ;
Dong, Mianxiong .
IEEE WIRELESS COMMUNICATIONS, 2023, 30 (04) :154-160
[10]  
Highways-England, 2017, Highways england webtris