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A Blockchain-Based Efficient Cross-Domain Authentication Scheme for internet of vehicles

Computers, Materials & Continua 2024年第7期80卷 567-585页

作者： Feng Zhao Hongtao Ding Chunhai Li Zhaoyu Su Guoling Liang Changsong Yang Guangxi Engineering Research Center of Industrial internet Security and Blockchain Guilin University of Electronic TechnologyGuilin541004China School of Information and Communication Guilin University of Electronic TechnologyGuilin541004China School of Computer Science and Information Security Guilin University of Electronic TechnologyGuilin541004China

The internet of vehicles(IoV)is extensively deployed in outdoor and open environments to effectively address traffic efficiency and safety issues by connecting vehicles to the ***,due to the open and variable nature of its network topology,vehicles frequently engage in cross-domain *** such processes,directly uploading sensitive information to roadside units for interaction may expose it to malicious tampering or interception by attackers,thus compromising the security of the cross-domain authentication ***,IoV imposes high real-time requirements,and existing cross-domain authentication schemes for IoV often encounter efficiency *** mitigate these challenges,we propose CAIoV,a blockchain-based efficient cross-domain authentication scheme for *** scheme comprehensively integrates technologies such as zero-knowledge proofs,smart contracts,and Merkle hash tree *** divides the cross-domain process into anonymous cross-domain authentication and safe cross-domain authentication phases to ensure efficiency while maintaining a balance between efficiency and ***,we evaluate the performance of *** results demonstrate that our proposed scheme reduces computational overhead by approximately 20%,communication overhead by around 10%,and storage overhead by nearly 30%.

关键词： Blockchain cross-domain authentication internet of vehicle zero-knowledge proof

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IoV environment exploring coordination:A federated learning approach

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Digital Communications and Networks 2024年第1期10卷 135-141页

作者： Tong Ding Lei Liu Yi Zhu Lizhen Cui Zhongmin Yan School of Software Shandong UniversityJinan250101China

Exploring open fields with coordinated unmanned vehicles is popular in academia and *** of the most impressive applicable approaches is the internet of vehicles(lov).The IoV connects vehicles,road infrastructures and communication facilities to provide solutions for exploration ***,the coordination of acquiring information from multi-vehicles may risk data *** this end,sharing high-quality experiences instead of raw data has become an urgent *** paper employs a Deep Reinforcement Learning(DRL)method to enable IoVs to generate training data with prioritized experience and states,which can support the IoV to explore the environment more ***,a Federated Learning(FL)experience sharing model is established to guarantee the vehicles'*** numerical results show that the proposed method presents a better successful sharing rate and a more stable convergence within the comparison of fundamental *** experiments also suggest that the proposed method could support agents without full information to achieve the tasks.

关键词： internet of vehicle Deep reinforcement learning Federated learning Data privacy

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Review on Offloading of vehicle Edge Computing

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Journal of Artificial Intelligence and Technology 2022年第4期2卷 132-143页

作者： Mingwei Wang Hualing Yi Feng Jiang Ling Lin and Min Gao Chongqing Business Vocational College ChongqingChina Chongqing Chang’an Automobile Co. LtdChongqingChina Chongqing University ChongqingChina

vehicle Edge Computing(VEC)is a new technology that can extend computing and storage functions to the edge of the internet of Things *** limited computing power and delay-sensitive mobile applications on the internet of vehicles(IoV),it is important to offload computing tasks to the end of the VEC ***,high mobility data security and privacy resource management and the randomness of IoV brought about new problems to the offloading of *** this end,this study focuses on the offloading of computing tasks in *** survey principal offloading schemes and methods in the VEC field and classify the current offloading of computing tasks into different *** also discuss the prospect of *** survey could give a reference for researchers to find and understand the essential characteristics of VEC,which helps choose the optimal solutions for the offloading of computing tasks in VEC.

关键词： computing offloading internet of vehicle vehicle edge computing

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Visible light communication for vehicle to Everything beyond 1 Gb/s based on an LED car headlight and a 2 × 2 PIN array

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Chinese Optics Letters 2020年第11期18卷 32-37页

作者：王超凡李国强胡昉辰赵一衡贾俊连邹鹏卢秋仪陈将李忠亚迟楠 Academy for Engineering and Technology Shanghai Institute for Advanced Communication and Data ScienceKey Laboratory for Information Science of Electromagnetic Waves(MoE)Fudan UniversityShanghai 200433China

Visible light communication(VLC) shows great potential in internet of vehicle applications. A single-input multi-output VLC system for vehicle to Everything is proposed and demonstrated. A commercial car headlight is used as transmitter. With a self-designed 2 × 2 positive-intrinsic-negative(PIN) array, four independent signals are received and equalized by deep-neural-network post-equalizers, respectively. Maximum-ratio combining brings high signal-to-noise ratio and data rate gain. The transmission data rate reaches 1.25 Gb/s at 1 m and exceeds 1 Gb/s at 4 m. To the best of our knowledge, it is the first-time demonstration of beyond 1 Gb/s employing a commercial car headlight.

关键词： visible light communication internet of vehicle vehicle to Everything single-input multi-output deep neural networks maximum-ratio combining

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Bus Arrival Time Prediction Based on Mixed Model

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China Communications 2017年第5期14卷 38-47页

作者： Jinglin Li Jie Gao Yu Yang Heran Wei State Key Laboratory of Network and Switching Technology Beijing University of Posts and TelecommunicationsBeijing 100876China

How to predict the bus arrival time accurately is a crucial problem to be solved in internet of vehicle. Existed methods cannot solve the problem effectively for ignoring the traffic delay jitter. In this paper,a three-stage mixed model is proposed for bus arrival time prediction. The first stage is pattern training. In this stage,the traffic delay jitter patterns(TDJP)are mined by K nearest neighbor and K-means in the historical traffic time data. The second stage is the single-step prediction,which is based on real-time adjusted Kalman filter with a modification of historical TDJP. In the third stage,as the influence of historical law is increasing in long distance prediction,we combine the single-step prediction dynamically with Markov historical transfer model to conduct the multi-step prediction. The experimental results show that the proposed single-step prediction model performs better in accuracy and efficiency than short-term traffic flow prediction and dynamic Kalman filter. The multi-step prediction provides a higher level veracity and reliability in travel time forecasting than short-term traffic flow and historical traffic pattern prediction models.

关键词： bus arrival time prediction traffic delay jitter pattern internet of vehicle

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