Yi Yang, Wenqiang Ma, Wenqiao Sun, Haibin Zhang t, Zhiqiang Liu, Lexi Xu, Ye Zhu
{"title":"车辆边缘计算网络的隐私保护数字孪生","authors":"Yi Yang, Wenqiang Ma, Wenqiao Sun, Haibin Zhang t, Zhiqiang Liu, Lexi Xu, Ye Zhu","doi":"10.1109/SmartWorld-UIC-ATC-ScalCom-DigitalTwin-PriComp-Metaverse56740.2022.00318","DOIUrl":null,"url":null,"abstract":"As an emerging technology, digital twin (DT) has great potential to address the challenges posed by the dynamics and complexity of vehicles in vehicular edge computing (VEC) networks. By mapping the VEC network to the virtual space, DT can monitor vehicles, road side units (RSUs), channels, and resource usage in real time, further bringing comprehensive and accurate network analysis to the VEC network. However, the real-world implement of DT-empowered VEC networks cannot avoid the collection of privacy-sensitive information of participants. An incentive mechanism is necessitated to identify the qualities of participants without prior information and incent them to participate in DT modeling, so as to realize the requirement of privacy preserving while improving the DT modeling efficiency. In this paper, We propose a combined multi-armed bandit-based auction (CMABA) incentive mechanism that can identify the quality of clients in the VEC network without revealing sensitive and private information, and achieve the optimal performance of the model under budget constraints. The simulation results show that this scheme can significantly incent high-quality clients to participate in DT modeling under the requirement of privacy preserving and the constraint of limited budget, and improve the accuracy of DT modeling.","PeriodicalId":43791,"journal":{"name":"Scalable Computing-Practice and Experience","volume":"23 1","pages":"2238-2243"},"PeriodicalIF":0.9000,"publicationDate":"2022-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Privacy-Preserving Digital Twin for Vehicular Edge Computing Networks\",\"authors\":\"Yi Yang, Wenqiang Ma, Wenqiao Sun, Haibin Zhang t, Zhiqiang Liu, Lexi Xu, Ye Zhu\",\"doi\":\"10.1109/SmartWorld-UIC-ATC-ScalCom-DigitalTwin-PriComp-Metaverse56740.2022.00318\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"As an emerging technology, digital twin (DT) has great potential to address the challenges posed by the dynamics and complexity of vehicles in vehicular edge computing (VEC) networks. By mapping the VEC network to the virtual space, DT can monitor vehicles, road side units (RSUs), channels, and resource usage in real time, further bringing comprehensive and accurate network analysis to the VEC network. However, the real-world implement of DT-empowered VEC networks cannot avoid the collection of privacy-sensitive information of participants. An incentive mechanism is necessitated to identify the qualities of participants without prior information and incent them to participate in DT modeling, so as to realize the requirement of privacy preserving while improving the DT modeling efficiency. In this paper, We propose a combined multi-armed bandit-based auction (CMABA) incentive mechanism that can identify the quality of clients in the VEC network without revealing sensitive and private information, and achieve the optimal performance of the model under budget constraints. The simulation results show that this scheme can significantly incent high-quality clients to participate in DT modeling under the requirement of privacy preserving and the constraint of limited budget, and improve the accuracy of DT modeling.\",\"PeriodicalId\":43791,\"journal\":{\"name\":\"Scalable Computing-Practice and Experience\",\"volume\":\"23 1\",\"pages\":\"2238-2243\"},\"PeriodicalIF\":0.9000,\"publicationDate\":\"2022-12-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Scalable Computing-Practice and Experience\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/SmartWorld-UIC-ATC-ScalCom-DigitalTwin-PriComp-Metaverse56740.2022.00318\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q4\",\"JCRName\":\"COMPUTER SCIENCE, SOFTWARE ENGINEERING\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Scalable Computing-Practice and Experience","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/SmartWorld-UIC-ATC-ScalCom-DigitalTwin-PriComp-Metaverse56740.2022.00318","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"COMPUTER SCIENCE, SOFTWARE ENGINEERING","Score":null,"Total":0}
Privacy-Preserving Digital Twin for Vehicular Edge Computing Networks
As an emerging technology, digital twin (DT) has great potential to address the challenges posed by the dynamics and complexity of vehicles in vehicular edge computing (VEC) networks. By mapping the VEC network to the virtual space, DT can monitor vehicles, road side units (RSUs), channels, and resource usage in real time, further bringing comprehensive and accurate network analysis to the VEC network. However, the real-world implement of DT-empowered VEC networks cannot avoid the collection of privacy-sensitive information of participants. An incentive mechanism is necessitated to identify the qualities of participants without prior information and incent them to participate in DT modeling, so as to realize the requirement of privacy preserving while improving the DT modeling efficiency. In this paper, We propose a combined multi-armed bandit-based auction (CMABA) incentive mechanism that can identify the quality of clients in the VEC network without revealing sensitive and private information, and achieve the optimal performance of the model under budget constraints. The simulation results show that this scheme can significantly incent high-quality clients to participate in DT modeling under the requirement of privacy preserving and the constraint of limited budget, and improve the accuracy of DT modeling.
期刊介绍:
The area of scalable computing has matured and reached a point where new issues and trends require a professional forum. SCPE will provide this avenue by publishing original refereed papers that address the present as well as the future of parallel and distributed computing. The journal will focus on algorithm development, implementation and execution on real-world parallel architectures, and application of parallel and distributed computing to the solution of real-life problems.