With the gradual development of the 5G industry network and applications,each industry application has various network performance requirements,while customers hope to upgrade their industrial structures by leveraging...With the gradual development of the 5G industry network and applications,each industry application has various network performance requirements,while customers hope to upgrade their industrial structures by leveraging 5G technologies.The guarantee of service level agreement(SLA)requirements is becoming more and more important,especially SLA performance indicators,such as delay,jitter,bandwidth,etc.For network operators to fulfill customer’s requirements,emerging network technologies such as time-sensitive networking(TSN),edge computing(EC)and network slicing are introduced into the mobile network to improve network performance,which increase the complexity of the network operation and maintenance(O&M),as well as the network cost.As a result,operators urgently need new solutions to achieve low-cost and high-efficiency network SLA management.In this paper,a digital twin network(DTN)solution is innovatively proposed to achieve the mapping and full lifecycle management of the end-to-end physical network.All the network operation policies such as configuration and modification can be generated and verified inside the digital twin network first to make sure that the SLA requirements can be fulfilled without affecting the related network environment and the performance of the other network services,making network operation and maintenance more effective and accurate.展开更多
In recent years,immense developments have occurred in the field of Artificial Intelligence(AI)and the spread of broadband and ubiquitous connectivity technologies.This has led to the development and commercialization ...In recent years,immense developments have occurred in the field of Artificial Intelligence(AI)and the spread of broadband and ubiquitous connectivity technologies.This has led to the development and commercialization of Digital Twin(DT)technology.The widespread adoption of DT has resulted in a new network paradigm called Digital Twin Networks(DTNs),which orchestrate through the networks of ubiquitous DTs and their corresponding physical assets.DTNs create virtual twins of physical objects via DT technology and realize the co-evolution between physical and virtual spaces through data processing,computing,and DT modeling.The high volume of user data and the ubiquitous communication systems in DTNs come with their own set of challenges.The most serious issue here is with respect to user data privacy and security because users of most applications are unaware of the data that they are sharing with these platforms and are naive in understanding the implications of the data breaches.Also,currently,there is not enough literature that focuses on privacy and security issues in DTN applications.In this survey,we first provide a clear idea of the components of DTNs and the common metrics used in literature to assess their performance.Next,we offer a standard network model that applies to most DTN applications to provide a better understanding of DTN’s complex and interleaved communications and the respective components.We then shed light on the common applications where DTNs have been adapted heavily and the privacy and security issues arising from the DTNs.We also provide different privacy and security countermeasures to address the previously mentioned issues in DTNs and list some state-of-the-art tools to mitigate the issues.Finally,we provide some open research issues and problems in the field of DTN privacy and security.展开更多
The proliferation of heterogeneous networks,such as the Internet of Things(IoT),unmanned aerial vehicle(UAV)networks,and edge networks,has increased the complexity of network operation and administration,driving the e...The proliferation of heterogeneous networks,such as the Internet of Things(IoT),unmanned aerial vehicle(UAV)networks,and edge networks,has increased the complexity of network operation and administration,driving the emergence of digital twin networks(DTNs)that create digital-physical network mappings.While DTNs enable performance analysis through emulation testbeds,current research focuses on network-level systems,neglecting equipment-level emulation of critical components like core switches and routers.To address this issue,we propose v Fabric(short for virtual switch),a digital twin emulator for high-capacity core switching equipment.This solution implements virtual switching and network processor(NP)chip models through specialized processes,deployable on single or distributed servers via socket communication.The v Fabric emulator can realize the accurate emulation for the core switching equipment with 720 ports and 100 Gbit/s per port on the largest scale.To our knowledge,this represents the first digital twin emulation framework specifically designed for large-capacity core switching equipment in communication networks.展开更多
This study was undertaken to determine the accuracy of using Ultrasound (US) estimation of twin fetuses by use of Artificial Neural Network. At First, as the training group, we performed US examinations on 186 healthy...This study was undertaken to determine the accuracy of using Ultrasound (US) estimation of twin fetuses by use of Artificial Neural Network. At First, as the training group, we performed US examinations on 186 healthy singleton fetuses within 3 days of delivery. Three input variables were used to construct the ANN model: abdominal circumference (AC), ab-dominal diameter (AD), biparietal diameter (BPD). Then, a total of 121 twin fetuses were assessed sub-sequently as the validation group. In validation group, the mean absolute error and the mean absolute per-cent error between estimated fetal weight and actual fetal weight was 261.77 g and 7.81%, respectively. Results show that, twin estimation of birth weight by ultrasound correlates fairly well with the actual weights of twin fetuses.展开更多
针对数据中心不间断电源(Uninterruptible Power Supply,UPS)系统运行成本高、运维滞后、能效低的问题,提出一套智能化升级框架,构建基于“感知-分析-决策-执行”闭环逻辑的4层系统架构。通过融合数字孪生建模、流式数据处理与机器学习...针对数据中心不间断电源(Uninterruptible Power Supply,UPS)系统运行成本高、运维滞后、能效低的问题,提出一套智能化升级框架,构建基于“感知-分析-决策-执行”闭环逻辑的4层系统架构。通过融合数字孪生建模、流式数据处理与机器学习推理,实现UPS运行状态的精细感知、健康预测与自适应调控。在平台层部署的扩展卡尔曼滤波与贝叶斯网络模型,可显著提升电池健康状态(State of Health,SOH)估算精度与故障定位能力。实证验证显示,该方案在电源使用效率(Power Usage Effectiveness,PUE)、平均维修时间、能耗及运维成本等关键指标上取得了显著优化,验证了其工程实用性与推广价值。展开更多
基金This work was supported by the National Key Research and Development Program of China(2020YFB1806801,2020YFB1806800)the National Natural Science Foundation of China(61773382).
文摘With the gradual development of the 5G industry network and applications,each industry application has various network performance requirements,while customers hope to upgrade their industrial structures by leveraging 5G technologies.The guarantee of service level agreement(SLA)requirements is becoming more and more important,especially SLA performance indicators,such as delay,jitter,bandwidth,etc.For network operators to fulfill customer’s requirements,emerging network technologies such as time-sensitive networking(TSN),edge computing(EC)and network slicing are introduced into the mobile network to improve network performance,which increase the complexity of the network operation and maintenance(O&M),as well as the network cost.As a result,operators urgently need new solutions to achieve low-cost and high-efficiency network SLA management.In this paper,a digital twin network(DTN)solution is innovatively proposed to achieve the mapping and full lifecycle management of the end-to-end physical network.All the network operation policies such as configuration and modification can be generated and verified inside the digital twin network first to make sure that the SLA requirements can be fulfilled without affecting the related network environment and the performance of the other network services,making network operation and maintenance more effective and accurate.
基金supported in part by the National Science Foundation(NSF)of the USA(2146497,2416872,2315596 and 2244219).
文摘In recent years,immense developments have occurred in the field of Artificial Intelligence(AI)and the spread of broadband and ubiquitous connectivity technologies.This has led to the development and commercialization of Digital Twin(DT)technology.The widespread adoption of DT has resulted in a new network paradigm called Digital Twin Networks(DTNs),which orchestrate through the networks of ubiquitous DTs and their corresponding physical assets.DTNs create virtual twins of physical objects via DT technology and realize the co-evolution between physical and virtual spaces through data processing,computing,and DT modeling.The high volume of user data and the ubiquitous communication systems in DTNs come with their own set of challenges.The most serious issue here is with respect to user data privacy and security because users of most applications are unaware of the data that they are sharing with these platforms and are naive in understanding the implications of the data breaches.Also,currently,there is not enough literature that focuses on privacy and security issues in DTN applications.In this survey,we first provide a clear idea of the components of DTNs and the common metrics used in literature to assess their performance.Next,we offer a standard network model that applies to most DTN applications to provide a better understanding of DTN’s complex and interleaved communications and the respective components.We then shed light on the common applications where DTNs have been adapted heavily and the privacy and security issues arising from the DTNs.We also provide different privacy and security countermeasures to address the previously mentioned issues in DTNs and list some state-of-the-art tools to mitigate the issues.Finally,we provide some open research issues and problems in the field of DTN privacy and security.
基金supported in part by the National Natural Science Foundation of China(NSFC)under Grant Nos.62171085,62272428,62001087,U20A20156,and 61871097the ZTE Industry-University-Institute Cooperation Funds under Grant No.HC-CN-20220722010。
文摘The proliferation of heterogeneous networks,such as the Internet of Things(IoT),unmanned aerial vehicle(UAV)networks,and edge networks,has increased the complexity of network operation and administration,driving the emergence of digital twin networks(DTNs)that create digital-physical network mappings.While DTNs enable performance analysis through emulation testbeds,current research focuses on network-level systems,neglecting equipment-level emulation of critical components like core switches and routers.To address this issue,we propose v Fabric(short for virtual switch),a digital twin emulator for high-capacity core switching equipment.This solution implements virtual switching and network processor(NP)chip models through specialized processes,deployable on single or distributed servers via socket communication.The v Fabric emulator can realize the accurate emulation for the core switching equipment with 720 ports and 100 Gbit/s per port on the largest scale.To our knowledge,this represents the first digital twin emulation framework specifically designed for large-capacity core switching equipment in communication networks.
文摘This study was undertaken to determine the accuracy of using Ultrasound (US) estimation of twin fetuses by use of Artificial Neural Network. At First, as the training group, we performed US examinations on 186 healthy singleton fetuses within 3 days of delivery. Three input variables were used to construct the ANN model: abdominal circumference (AC), ab-dominal diameter (AD), biparietal diameter (BPD). Then, a total of 121 twin fetuses were assessed sub-sequently as the validation group. In validation group, the mean absolute error and the mean absolute per-cent error between estimated fetal weight and actual fetal weight was 261.77 g and 7.81%, respectively. Results show that, twin estimation of birth weight by ultrasound correlates fairly well with the actual weights of twin fetuses.
文摘针对数据中心不间断电源(Uninterruptible Power Supply,UPS)系统运行成本高、运维滞后、能效低的问题,提出一套智能化升级框架,构建基于“感知-分析-决策-执行”闭环逻辑的4层系统架构。通过融合数字孪生建模、流式数据处理与机器学习推理,实现UPS运行状态的精细感知、健康预测与自适应调控。在平台层部署的扩展卡尔曼滤波与贝叶斯网络模型,可显著提升电池健康状态(State of Health,SOH)估算精度与故障定位能力。实证验证显示,该方案在电源使用效率(Power Usage Effectiveness,PUE)、平均维修时间、能耗及运维成本等关键指标上取得了显著优化,验证了其工程实用性与推广价值。
