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A lightweight dual authentication scheme for V2V communication in 6G-based vanets 认领 引用
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作者 Xia Feng Yaru Wang +1 位作者 Kaiping Cui Liangmin Wang 《Digital Communications and Networks》 SCIE EI CSCD 2025年第4期1224-1235,共12页
The advancement of 6G wireless communication technology has facilitated the integration of Vehicular Ad-hoc Networks(VANETs).However,the messages transmitted over the public channel in the open and dynamic VANETs are ... The advancement of 6G wireless communication technology has facilitated the integration of Vehicular Ad-hoc Networks(VANETs).However,the messages transmitted over the public channel in the open and dynamic VANETs are vulnerable to malicious attacks.Although numerous researchers have proposed authentication schemes to enhance the security of Vehicle-to-Vehicle(V2V)communication,most existing methodologies face two significant challenges:(1)the majority of the schemes are not lightweight enough to support realtime message interaction among vehicles;(2)the sensitive information like identity and position is at risk of being compromised.To tackle these issues,we propose a lightweight dual authentication protocol for V2V communication based on Physical Unclonable Function(PUF).The proposed scheme accomplishes dual authentication between vehicles by the combination of Zero-Knowledge Proof(ZKP)and MASK function.The security analysis proves that our scheme provides both anonymous authentication and information unlinkability.Additionally,the performance analysis demonstrates that the computation overhead of our scheme is approximately reduced 23.4% compared to the state-of-the-art schemes.The practical simulation conducted in a 6G network environment demonstrates the feasibility of 6G-based VANETs and their potential for future advancements. 展开更多
关键词 Vehicular ad-hoc network Identity authentication Physical unclonable function MASK function Zero-knowledge proof 6G wireless communication technology
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A Novel 6G Scalable Blockchain Clustering-Based Computer Vision Character Detection for Mobile Images 认领 引用 被引量：1
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作者 Yuejie Li Shijun Li 《Computers, Materials & Continua》 SCIE EI 2024年第3期3041-3070,共30页
6G is envisioned as the next generation of wireless communication technology,promising unprecedented data speeds,ultra-low Latency,and ubiquitous Connectivity.In tandem with these advancements,blockchain technology is... 6G is envisioned as the next generation of wireless communication technology,promising unprecedented data speeds,ultra-low Latency,and ubiquitous Connectivity.In tandem with these advancements,blockchain technology is leveraged to enhance computer vision applications’security,trustworthiness,and transparency.With the widespread use of mobile devices equipped with cameras,the ability to capture and recognize Chinese characters in natural scenes has become increasingly important.Blockchain can facilitate privacy-preserving mechanisms in applications where privacy is paramount,such as facial recognition or personal healthcare monitoring.Users can control their visual data and grant or revoke access as needed.Recognizing Chinese characters from images can provide convenience in various aspects of people’s lives.However,traditional Chinese character text recognition methods often need higher accuracy,leading to recognition failures or incorrect character identification.In contrast,computer vision technologies have significantly improved image recognition accuracy.This paper proposed a Secure end-to-end recognition system(SE2ERS)for Chinese characters in natural scenes based on convolutional neural networks(CNN)using 6G technology.The proposed SE2ERS model uses the Weighted Hyperbolic Curve Cryptograph(WHCC)of the secure data transmission in the 6G network with the blockchain model.The data transmission within the computer vision system,with a 6G gradient directional histogram(GDH),is employed for character estimation.With the deployment of WHCC and GDH in the constructed SE2ERS model,secure communication is achieved for the data transmission with the 6G network.The proposed SE2ERS compares the performance of traditional Chinese text recognition methods and data transmission environment with 6G communication.Experimental results demonstrate that SE2ERS achieves an average recognition accuracy of 88%for simple Chinese characters,compared to 81.2%with traditional methods.For complex Chinese characters,the average recognition accuracy improves to 84.4%with our system,compared to 72.8%with traditional methods.Additionally,deploying the WHCC model improves data security with the increased data encryption rate complexity of∼12&higher than the traditional techniques. 展开更多
关键词 6G technology blockchain end-to-end recognition Chinese characters natural scene computer vision algorithms convolutional neural network
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Spatio-Temporal Cellular Network Traffic Prediction Using Multi-Task Deep Learning for AI-Enabled 6G 认领 引用 被引量：1
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作者 Xiaochuan Sun Biao Wei +3 位作者 Jiahui Gao Difei Cao Zhigang Li Yingqi Li 《Journal of Beijing Institute of Technology》 EI CAS 2022年第5期441-453,共13页
Spatio-temporal cellular network traffic prediction at wide-area level plays an important role in resource reconfiguration,traffic scheduling and intrusion detection,thus potentially supporting connected intelligence ... Spatio-temporal cellular network traffic prediction at wide-area level plays an important role in resource reconfiguration,traffic scheduling and intrusion detection,thus potentially supporting connected intelligence of the sixth generation of mobile communications technology(6G).However,the existing studies just focus on the spatio-temporal modeling of traffic data of single network service,such as short message,call,or Internet.It is not conducive to accurate prediction of traffic data,characterised by diverse network service,spatio-temporality and supersize volume.To address this issue,a novel multi-task deep learning framework is developed for citywide cellular network traffic prediction.Functionally,this framework mainly consists of a dual modular feature sharing layer and a multi-task learning layer(DMFS-MT).The former aims at mining long-term spatio-temporal dependencies and local spatio-temporal fluctuation trends in data,respectively,via a new combination of convolutional gated recurrent unit(ConvGRU)and 3-dimensional convolutional neural network(3D-CNN).For the latter,each task is performed for predicting service-specific traffic data based on a fully connected network.On the real-world Telecom Italia dataset,simulation results demonstrate the effectiveness of our proposal through prediction performance measure,spatial pattern comparison and statistical distribution verification. 展开更多
关键词 the sixth generation of mobile communications technology(6G) cellular network traffic multi-task deep learning spatio-temporality
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