A Blockchain-Based Efficient Cross-Domain Authentication Scheme for Internet of Vehicles

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 network.However,due to the open and variable nature of its network topology,vehicles frequently engage in cross-domain interactions.During 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 process.Additionally,IoV imposes high real-time requirements,and existing cross-domain authentication schemes for IoV often encounter efficiency issues.To mitigate these challenges,we propose CAIoV,a blockchain-based efficient cross-domain authentication scheme for IoV.This scheme comprehensively integrates technologies such as zero-knowledge proofs,smart contracts,and Merkle hash tree structures.It 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 security.Finally,we evaluate the performance of CAIoV.Experimental results demonstrate that our proposed scheme reduces computational overhead by approximately 20%,communication overhead by around 10%,and storage overhead by nearly 30%.

Review on Offloading of Vehicle Edge Computing

Vehicle Edge Computing(VEC)is a new technology that can extend computing and storage functions to the edge of the Internet of Things systems.For 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 network.Still,high mobility data security and privacy resource management and the randomness of IoV brought about new problems to the offloading of VEC.To this end,this study focuses on the offloading of computing tasks in VEC.We survey principal offloading schemes and methods in the VEC field and classify the current offloading of computing tasks into different categories.We also discuss the prospect of VEC.This 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.

Applied Research of IoV in Insurance Industry

A vehicle intelligent terminal is designed to collect vehicle state information and driver behavior information based on the application of Internet of Vehicle in the insurance industry, and data of 12 risk factors affecting the traffic safety is extracted from the information. The Principal Component Analysis is improved and PCA based on the index significant priority is proposed, using which the data of risk factors is processed and 3 principal components are obtained as the pricing factor of the insurance industry.

IoV environment exploring coordination:A federated learning approach

Exploring open fields with coordinated unmanned vehicles is popular in academia and industries.One 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 tasks.However,the coordination of acquiring information from multi-vehicles may risk data privacy.To this end,sharing high-quality experiences instead of raw data has become an urgent demand.This 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 efficiently.Moreover,a Federated Learning(FL)experience sharing model is established to guarantee the vehicles'privacy.The numerical results show that the proposed method presents a better successful sharing rate and a more stable convergence within the comparison of fundamental methods.The experiments also suggest that the proposed method could support agents without full information to achieve the tasks.

V2I Physical Layer Security Beamforming with Antenna Hardware Impairments under RIS Assistance

The Internet of Vehicles(IoV)will carry a large amount of security and privacy-related data,which makes the secure communication between the IoV terminals increasingly critical.This paper studies the joint beamforming for physical-layer security transmission in the coexistence of Vehicle-to-Infrastructure(V2I)and Vehicle-toVehicle(V2V)communication with Reconfigurable Intelligent Surface(RIS)assistance,taking into account hardware impairments.A communication model for physical-layer security transmission is established when the eavesdropping user is present and the base station antenna has hardware impairments assisted by RIS.Based on this model,we propose to maximize the V2I physical-layer security transmission rate.To solve the coupled non-convex optimization problem,an alternating optimization algorithm based on second-order cone programming and semidefinite relaxation is proposed to obtain the optimal V2I base station transmit precoding and RIS reflect phase shift matrix.Finally,simulation results are presented to verify the convergence and superiority of our proposed algorithm while analyzing the impact of system parameters on the V2I physical-layer security transmission rate.The simulation results further demonstrate that the proposed robust beamforming algorithm considering hardware impairments will achieve an average performance improvement of 0.7 dB over a non-robustly designed algorithm.Furthermore,increasing the number of RIS reflective units from 10 to 50 results in an almost 2 dB enhancement in secure transmission rate.

Bus Arrival Time Prediction Based on Mixed Model

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.

Computing Paradigms in Emerging Vehicular Environments:A Review

Determining how to structure vehicular network environments can be done in various ways.Here,we highlight vehicle networks’evolution from vehicular ad-hoc networks(VANET)to the internet of vehicles(Io Vs),listing their benefits and limitations.We also highlight the reasons in adopting wireless technologies,in particular,IEEE 802.11 p and 5 G vehicle-toeverything,as well as the use of paradigms able to store and analyze a vast amount of data to produce intelligence and their applications in vehicular environments.We also correlate the use of each of these paradigms with the desire to meet existing intelligent transportation systems’requirements.The presentation of each paradigm is given from a historical and logical standpoint.In particular,vehicular fog computing improves on the deficiences of vehicular cloud computing,so both are not exclusive from the application point of view.We also emphasize some security issues that are linked to the characteristics of these paradigms and vehicular networks,showing that they complement each other and share problems and limitations.As these networks still have many opportunities to grow in both concept and application,we finally discuss concepts and technologies that we believe are beneficial.Throughout this work,we emphasize the crucial role of these concepts for the well-being of humanity.

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

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.