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Deep Transfer Learning Techniques in Intrusion Detection System-Internet of Vehicles: A State-of-the-Art Review
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作者 Wufei Wu Javad Hassannataj Joloudari +8 位作者 Senthil Kumar Jagatheesaperumal Kandala N.V.P.SRajesh Silvia Gaftandzhieva Sadiq Hussain Rahimullah Rabih Najibullah Haqjoo Mobeen Nazar Hamed Vahdat-Nejad Rositsa Doneva 《Computers, Materials & Continua》 SCIE EI 2024年第8期2785-2813,共29页
The high performance of IoT technology in transportation networks has led to the increasing adoption of Internet of Vehicles(IoV)technology.The functional advantages of IoV include online communication services,accide... The high performance of IoT technology in transportation networks has led to the increasing adoption of Internet of Vehicles(IoV)technology.The functional advantages of IoV include online communication services,accident prevention,cost reduction,and enhanced traffic regularity.Despite these benefits,IoV technology is susceptible to cyber-attacks,which can exploit vulnerabilities in the vehicle network,leading to perturbations,disturbances,non-recognition of traffic signs,accidents,and vehicle immobilization.This paper reviews the state-of-the-art achievements and developments in applying Deep Transfer Learning(DTL)models for Intrusion Detection Systems in the Internet of Vehicles(IDS-IoV)based on anomaly detection.IDS-IoV leverages anomaly detection through machine learning and DTL techniques to mitigate the risks posed by cyber-attacks.These systems can autonomously create specific models based on network data to differentiate between regular traffic and cyber-attacks.Among these techniques,transfer learning models are particularly promising due to their efficacy with tagged data,reduced training time,lower memory usage,and decreased computational complexity.We evaluate DTL models against criteria including the ability to transfer knowledge,detection rate,accurate analysis of complex data,and stability.This review highlights the significant progress made in the field,showcasing how DTL models enhance the performance and reliability of IDS-IoV systems.By examining recent advancements,we provide insights into how DTL can effectively address cyber-attack challenges in IoV environments,ensuring safer and more efficient transportation networks. 展开更多
关键词 Cyber-attacks internet of things internet of vehicles intrusion detection system
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Low-Cost Federated Broad Learning for Privacy-Preserved Knowledge Sharing in the RIS-Aided Internet of Vehicles 被引量:1
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作者 Xiaoming Yuan Jiahui Chen +4 位作者 Ning Zhang Qiang(John)Ye Changle Li Chunsheng Zhu Xuemin Sherman Shen 《Engineering》 SCIE EI CAS CSCD 2024年第2期178-189,共12页
High-efficiency and low-cost knowledge sharing can improve the decision-making ability of autonomous vehicles by mining knowledge from the Internet of Vehicles(IoVs).However,it is challenging to ensure high efficiency... High-efficiency and low-cost knowledge sharing can improve the decision-making ability of autonomous vehicles by mining knowledge from the Internet of Vehicles(IoVs).However,it is challenging to ensure high efficiency of local data learning models while preventing privacy leakage in a high mobility environment.In order to protect data privacy and improve data learning efficiency in knowledge sharing,we propose an asynchronous federated broad learning(FBL)framework that integrates broad learning(BL)into federated learning(FL).In FBL,we design a broad fully connected model(BFCM)as a local model for training client data.To enhance the wireless channel quality for knowledge sharing and reduce the communication and computation cost of participating clients,we construct a joint resource allocation and reconfigurable intelligent surface(RIS)configuration optimization framework for FBL.The problem is decoupled into two convex subproblems.Aiming to improve the resource scheduling efficiency in FBL,a double Davidon–Fletcher–Powell(DDFP)algorithm is presented to solve the time slot allocation and RIS configuration problem.Based on the results of resource scheduling,we design a reward-allocation algorithm based on federated incentive learning(FIL)in FBL to compensate clients for their costs.The simulation results show that the proposed FBL framework achieves better performance than the comparison models in terms of efficiency,accuracy,and cost for knowledge sharing in the IoV. 展开更多
关键词 Knowledge sharing internet of vehicles Federated learning Broad learning Reconfigurable intelligent surfaces Resource allocation
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A credibility-aware swarm-federated deep learning framework in internet of vehicles 被引量:1
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作者 Zhe Wang Xinhang Li +2 位作者 Tianhao Wu Chen Xu Lin Zhang 《Digital Communications and Networks》 SCIE CSCD 2024年第1期150-157,共8页
Although Federated Deep Learning(FDL)enables distributed machine learning in the Internet of Vehicles(IoV),it requires multiple clients to upload model parameters,thus still existing unavoidable communication overhead... Although Federated Deep Learning(FDL)enables distributed machine learning in the Internet of Vehicles(IoV),it requires multiple clients to upload model parameters,thus still existing unavoidable communication overhead and data privacy risks.The recently proposed Swarm Learning(SL)provides a decentralized machine learning approach for unit edge computing and blockchain-based coordination.A Swarm-Federated Deep Learning framework in the IoV system(IoV-SFDL)that integrates SL into the FDL framework is proposed in this paper.The IoV-SFDL organizes vehicles to generate local SL models with adjacent vehicles based on the blockchain empowered SL,then aggregates the global FDL model among different SL groups with a credibility weights prediction algorithm.Extensive experimental results show that compared with the baseline frameworks,the proposed IoV-SFDL framework reduces the overhead of client-to-server communication by 16.72%,while the model performance improves by about 5.02%for the same training iterations. 展开更多
关键词 Swarm learning Federated deep learning internet of vehicles PRIVACY EFFICIENCY
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3D Road Network Modeling and Road Structure Recognition in Internet of Vehicles
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作者 Dun Cao Jia Ru +3 位作者 Jian Qin Amr Tolba Jin Wang Min Zhu 《Computer Modeling in Engineering & Sciences》 SCIE EI 2024年第2期1365-1384,共20页
Internet of Vehicles (IoV) is a new system that enables individual vehicles to connect with nearby vehicles,people, transportation infrastructure, and networks, thereby realizing amore intelligent and efficient transp... Internet of Vehicles (IoV) is a new system that enables individual vehicles to connect with nearby vehicles,people, transportation infrastructure, and networks, thereby realizing amore intelligent and efficient transportationsystem. The movement of vehicles and the three-dimensional (3D) nature of the road network cause the topologicalstructure of IoV to have the high space and time complexity.Network modeling and structure recognition for 3Droads can benefit the description of topological changes for IoV. This paper proposes a 3Dgeneral roadmodel basedon discrete points of roads obtained from GIS. First, the constraints imposed by 3D roads on moving vehicles areanalyzed. Then the effects of road curvature radius (Ra), longitudinal slope (Slo), and length (Len) on speed andacceleration are studied. Finally, a general 3D road network model based on road section features is established.This paper also presents intersection and road section recognition methods based on the structural features ofthe 3D road network model and the road features. Real GIS data from a specific region of Beijing is adopted tocreate the simulation scenario, and the simulation results validate the general 3D road network model and therecognitionmethod. Therefore, thiswork makes contributions to the field of intelligent transportation by providinga comprehensive approach tomodeling the 3Droad network and its topological changes in achieving efficient trafficflowand improved road safety. 展开更多
关键词 internet of vehicles road networks 3D road model structure recognition GIS
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Survey on digital twins for Internet of Vehicles:Fundamentals,challenges,and opportunities
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作者 Jiajie Guo Muhammad Bilal +3 位作者 Yuying Qiu Cheng Qian Xiaolong Xu Kim-Kwang Raymond Choo 《Digital Communications and Networks》 SCIE CSCD 2024年第2期237-247,共11页
As autonomous vehicles and the other supporting infrastructures(e.g.,smart cities and intelligent transportation systems)become more commonplace,the Internet of Vehicles(IoV)is getting increasingly prevalent.There hav... As autonomous vehicles and the other supporting infrastructures(e.g.,smart cities and intelligent transportation systems)become more commonplace,the Internet of Vehicles(IoV)is getting increasingly prevalent.There have been attempts to utilize Digital Twins(DTs)to facilitate the design,evaluation,and deployment of IoV-based systems,for example by supporting high-fidelity modeling,real-time monitoring,and advanced predictive capabilities.However,the literature review undertaken in this paper suggests that integrating DTs into IoV-based system design and deployment remains an understudied topic.In addition,this paper explains how DTs can benefit IoV system designers and implementers,as well as describes several challenges and opportunities for future researchers. 展开更多
关键词 internet of vehicles Digital twin Simulation Traffic systems
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Blockchain-Based Message Authentication Scheme for Internet of Vehicles in an Edge Computing Environment
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作者 Qiping Zou Zhong Ruan Huaning Song 《Computer Systems Science & Engineering》 2024年第5期1301-1328,共28页
As an important application of intelligent transportation system,Internet of Vehicles(IoV)provides great convenience for users.Users can obtain real-time traffic conditions through the IoV’s services,plan users’trav... As an important application of intelligent transportation system,Internet of Vehicles(IoV)provides great convenience for users.Users can obtain real-time traffic conditions through the IoV’s services,plan users’travel routes,and improve travel efficiency.However,in the IoV system,there are always malicious vehicle nodes publishing false information.Therefore,it is essential to ensure the legitimacy of the source.In addition,during the peak period of vehicle travel,the vehicle releases a large number of messages,and IoV authentication efficiency is prone to performance bottlenecks.Most existing authentication schemes have the problem of low authentication efficiency in the scenario.To address the above problems,this paper designs a novel reliable anonymous authentication scheme in IoV for Rush-hour Traffic.Here,our scheme uses blockchain and elliptic curve cryptography(ECC)to design authentication algorithms for message authentication between vehicles and roadside units(RSU).Additionally,we introduce the idea of edge computing into the scheme,RSU will select themost suitable vehicle as the edge computing node for message authentication.In addition,we used the ProVerif tool for Internet security protocols and applications to test its security,ensuring that it is secure under different network attacks.In the simulation experiment,we compare our scheme with other existing works.Our scheme has a significant improvement in computational overhead,authentication efficiency and packet loss rate,and is suitable for traffic scenarios with large message volume. 展开更多
关键词 internet of vehicles messages authentication edge computing blockchain elliptic curve cryptography
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Federated Learning with Blockchain for Privacy-Preserving Data Sharing in Internet of Vehicles 被引量:3
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作者 Wenxian Jiang Mengjuan Chen Jun Tao 《China Communications》 SCIE CSCD 2023年第3期69-85,共17页
Data sharing technology in Internet of Vehicles(Io V)has attracted great research interest with the goal of realizing intelligent transportation and traffic management.Meanwhile,the main concerns have been raised abou... Data sharing technology in Internet of Vehicles(Io V)has attracted great research interest with the goal of realizing intelligent transportation and traffic management.Meanwhile,the main concerns have been raised about the security and privacy of vehicle data.The mobility and real-time characteristics of vehicle data make data sharing more difficult in Io V.The emergence of blockchain and federated learning brings new directions.In this paper,a data-sharing model that combines blockchain and federated learning is proposed to solve the security and privacy problems of data sharing in Io V.First,we use federated learning to share data instead of exposing actual data and propose an adaptive differential privacy scheme to further balance the privacy and availability of data.Then,we integrate the verification scheme into the consensus process,so that the consensus computation can filter out low-quality models.Experimental data shows that our data-sharing model can better balance the relationship between data availability and privacy,and also has enhanced security. 展开更多
关键词 blockchain federated learning PRIVACY data sharing internet of vehicles
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Traffic Management in Internet of Vehicles Using Improved Ant Colony Optimization 被引量:2
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作者 Abida Sharif Imran Sharif +6 位作者 Muhammad Asim Saleem Muhammad Attique Khan Majed Alhaisoni Marriam Nawaz Abdullah Alqahtani Ye Jin Kim Byoungchol Chang 《Computers, Materials & Continua》 SCIE EI 2023年第6期5379-5393,共15页
The Internet of Vehicles(IoV)is a networking paradigm related to the intercommunication of vehicles using a network.In a dynamic network,one of the key challenges in IoV is traffic management under increasing vehicles... The Internet of Vehicles(IoV)is a networking paradigm related to the intercommunication of vehicles using a network.In a dynamic network,one of the key challenges in IoV is traffic management under increasing vehicles to avoid congestion.Therefore,optimal path selection to route traffic between the origin and destination is vital.This research proposed a realistic strategy to reduce traffic management service response time by enabling real-time content distribution in IoV systems using heterogeneous network access.Firstly,this work proposed a novel use of the Ant Colony Optimization(ACO)algorithm and formulated the path planning optimization problem as an Integer Linear Program(ILP).This integrates the future estimation metric to predict the future arrivals of the vehicles,searching the optimal routes.Considering the mobile nature of IOV,fuzzy logic is used for congestion level estimation along with the ACO to determine the optimal path.The model results indicate that the suggested scheme outperforms the existing state-of-the-art methods by identifying the shortest and most cost-effective path.Thus,this work strongly supports its use in applications having stringent Quality of Service(QoS)requirements for the vehicles. 展开更多
关键词 internet of vehicles internet of things fuzzy logic OPTIMIZATION path planning
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Container-Based Internet of Vehicles Edge Application Migration Mechanism 被引量:1
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作者 Sujie Shao Shihan Tian +1 位作者 Shaoyong Guo Xuesong Qiu 《Computers, Materials & Continua》 SCIE EI 2023年第6期4867-4891,共25页
Internet of Vehicles(IoV)applications integrating with edge com-puting will significantly drive the growth of IoV.However,the contradiction between the high-speed mobility of vehicles,the delay sensitivity of corre-sp... Internet of Vehicles(IoV)applications integrating with edge com-puting will significantly drive the growth of IoV.However,the contradiction between the high-speed mobility of vehicles,the delay sensitivity of corre-sponding IoV applications and the limited coverage and resource capacity of distributed edge servers will pose challenges to the service continuity and stability of IoV applications.IoV application migration is a promising solution that can be supported by application containerization,a technology for seamless cross-edge-server application migration without user perception.Therefore,this paper proposes the container-based IoV edge application migration mechanism,consisting of three parts.The first is the migration trigger determination algorithm for cross-border migration and service degra-dation migration,respectively,based on trajectory prediction and traffic awareness to improve the determination accuracy.The second is the migration target decision calculation model for minimizing the average migration time and maximizing the average service time to reduce migration times and improve the stability and adaptability of migration decisions.The third is the migration decision algorithm based on the improved artificial bee colony algorithm to avoid local optimal migration decisions.Simulation results show that the proposed migration mechanism can reduce migration times,reduce average migration time,improve average service time and enhance the stability and adaptability of IoV application services. 展开更多
关键词 Application migration CONTAINER internet of vehicles edge computing
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PGSLM:Edge-Enabled Probabilistic Graph Structure Learning Model for Traffic Forecasting in Internet of Vehicles
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作者 Xiaozhu Liu Jiaru Zeng +1 位作者 Rongbo Zhu Hao Liu 《China Communications》 SCIE CSCD 2023年第4期270-286,共17页
With the rapid development of the 5G communications,the edge intelligence enables Internet of Vehicles(IoV)to provide traffic forecasting to alleviate traffic congestion and improve quality of experience of users simu... With the rapid development of the 5G communications,the edge intelligence enables Internet of Vehicles(IoV)to provide traffic forecasting to alleviate traffic congestion and improve quality of experience of users simultaneously.To enhance the forecasting performance,a novel edge-enabled probabilistic graph structure learning model(PGSLM)is proposed,which learns the graph structure and parameters by the edge sensing information and discrete probability distribution on the edges of the traffic road network.To obtain the spatio-temporal dependencies of traffic data,the learned dynamic graphs are combined with a predefined static graph to generate the graph convolution part of the recurrent graph convolution module.During the training process,a new graph training loss is introduced,which is composed of the K nearest neighbor(KNN)graph constructed by the traffic feature tensors and the graph structure.Detailed experimental results show that,compared with existing models,the proposed PGSLM improves the traffic prediction performance in terms of average absolute error and root mean square error in IoV. 展开更多
关键词 edge computing traffic forecasting graph convolutional network graph structure learning internet of vehicles
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SDN-Enabled Content Dissemination Scheme for the Internet of Vehicles
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作者 Abida Sharif Muhammad Imran Sharif +5 位作者 Muhammad Attique Khan Nisar Ali Abdullah Alqahtani Majed Alhaisoni Ye Jin Kim Byoungchol Chang 《Computers, Materials & Continua》 SCIE EI 2023年第5期2383-2396,共14页
The content-centric networking(CCN)architecture allows access to the content through name,instead of the physical location where the content is stored,which makes it a more robust and flexible content-based architectu... The content-centric networking(CCN)architecture allows access to the content through name,instead of the physical location where the content is stored,which makes it a more robust and flexible content-based architecture.Nevertheless,in CCN,the broadcast nature of vehicles on the Internet of Vehicles(IoV)results in latency and network congestion.The IoVbased content distribution is an emerging concept in which all the vehicles are connected via the internet.Due to the high mobility of vehicles,however,IoV applications have different network requirements that differ from those of many other networks,posing new challenges.Considering this,a novel strategy mediator framework is presented in this paper for managing the network resources efficiently.Software-defined network(SDN)controller is deployed for improving the routing flexibility and facilitating in the interinteroperability of heterogeneous devices within the network.Due to the limited memory of edge devices,the delectable bloom filters are used for caching and storage.Finally,the proposed scheme is compared with the existing variants for validating its effectiveness. 展开更多
关键词 internet of vehicles(IOV) content dissemination multi mediator data traffic management
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Communication-Efficient Decision-Making of Digital Twin Assisted Internet of Vehicles: A Hierarchical Multi-Agent Reinforcement Learning Approach
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作者 Xiaoyuan Fu Quan Yuan +3 位作者 Shifan Liu Baozhu Li Qi Qi Jingyu Wang 《China Communications》 SCIE CSCD 2023年第3期55-68,共14页
The connected autonomous vehicle is considered an effective way to improve transport safety and efficiency.To overcome the limited sensing and computing capabilities of individual vehicles,we design a digital twin ass... The connected autonomous vehicle is considered an effective way to improve transport safety and efficiency.To overcome the limited sensing and computing capabilities of individual vehicles,we design a digital twin assisted decision-making framework for Internet of Vehicles,by leveraging the integration of communication,sensing and computing.In this framework,the digital twin entities residing on edge can effectively communicate and cooperate with each other to plan sub-targets for their respective vehicles,while the vehicles only need to achieve the sub-targets by generating a sequence of atomic actions.Furthermore,we propose a hierarchical multiagent reinforcement learning approach to implement the framework,which can be trained in an end-to-end way.In the proposed approach,the communication interval of digital twin entities could adapt to timevarying environment.Extensive experiments on driving decision-making have been performed in traffic junction scenarios of different difficulties.The experimental results show that the proposed approach can largely improve collaboration efficiency while reducing communication overhead. 展开更多
关键词 digital twin internet of vehicles hierar-chical reinforcement learning
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A Practical Intrusion Detection System for Internet of Vehicles 被引量:1
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作者 Wenliang Fu Xin Xin +1 位作者 Ping Guo Zhou Zhou 《China Communications》 SCIE CSCD 2016年第10期263-275,共13页
Internet of Vehicles(henceforth called IoV) is a public network system and high-value target for intrusions that may cause efficiency issues, privacy leakages or even physical damage. Conventional intrusion detection ... Internet of Vehicles(henceforth called IoV) is a public network system and high-value target for intrusions that may cause efficiency issues, privacy leakages or even physical damage. Conventional intrusion detection methods are normally designed for the Internet infrastructures which cannot directly apply in the context of IoV. This work proposes an FPGA based intrusion detection method that can not only achieve real-time scanning performance but also be applied in vehicular environment. We evaluate our scheme on a Xilinx FPGA based platform. Experiments show that the proposed system can achieve a throughput of more than 39 Gbps on existing FPGA platform which is about 15% higher than state-of-the-art techniques,and the total power consumption for the prototype is about 7.5 w. Moreover, the processing latency of the prototype is about 4 us and is about one sixtieth part of the popular software IDS systems. 展开更多
关键词 internet of vehicles intrusion detection VANET FPGA
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An Overview of Internet of Vehicles 被引量:58
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作者 YANGFangchun WANG Shangguang LI Jinglin LIU Zhihan SUN Qibo 《China Communications》 SCIE CSCD 2014年第10期1-15,共15页
The new era of the Internet of Things is driving the evolution of conventional Vehicle Ad-hoc Networks into the lnternet of Vehicles (IoV). With the rapid development of computation and communication technologies, l... The new era of the Internet of Things is driving the evolution of conventional Vehicle Ad-hoc Networks into the lnternet of Vehicles (IoV). With the rapid development of computation and communication technologies, loV promises huge commercial interest and research value, thereby attracting a large number of companies and researchers. This paper proposes an abstract network model of the IoV, discusses the technologies required to create the IoV, presents different applications based on certain currently existing technologies, provides several open research challenges and describes essential future research in the area of loV. 展开更多
关键词 internet of vehicles VANET vehicle telematics network model
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A Novel Load Balancing Strategy of Software-Defined Cloud/Fog Networking in the Internet of Vehicles 被引量:13
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作者 Xiuli He Zhiyuan Ren +1 位作者 Chenhua Shi Jian Fang 《China Communications》 SCIE CSCD 2016年第S2期140-149,共10页
The Internet of Vehicles(IoV)has been widely researched in recent years,and cloud computing has been one of the key technologies in the IoV.Although cloud computing provides high performance compute,storage and networ... The Internet of Vehicles(IoV)has been widely researched in recent years,and cloud computing has been one of the key technologies in the IoV.Although cloud computing provides high performance compute,storage and networking services,the IoV still suffers with high processing latency,less mobility support and location awareness.In this paper,we integrate fog computing and software defined networking(SDN) to address those problems.Fog computing extends computing and storing to the edge of the network,which could decrease latency remarkably in addition to enable mobility support and location awareness.Meanwhile,SDN provides flexible centralized control and global knowledge to the network.In order to apply the software defined cloud/fog networking(SDCFN) architecture in the IoV effectively,we propose a novel SDN-based modified constrained optimization particle swarm optimization(MPSO-CO) algorithm which uses the reverse of the flight of mutation particles and linear decrease inertia weight to enhance the performance of constrained optimization particle swarm optimization(PSO-CO).The simulation results indicate that the SDN-based MPSO-CO algorithm could effectively decrease the latency and improve the quality of service(QoS) in the SDCFN architecture. 展开更多
关键词 internet of vehicles cloud computing cloud/fog network software defined networking load balancing
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MADCR:Mobility Aware Dynamic Clustering-Based Routing Protocol in Internet of Vehicles 被引量:9
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作者 Sankar Sennan Somula Ramasubbareddy +3 位作者 Sathiyabhama Balasubramaniyam Anand Nayyar Chaker Abdelaziz Kerrache Muhammad Bilal 《China Communications》 SCIE CSCD 2021年第7期69-85,共17页
Internet of Vehicles(IoV)is an evolution of the Internet of Things(IoT)to improve the capabilities of vehicular ad-hoc networks(VANETs)in intelligence transport systems.The network topology in IoV paradigm is highly d... Internet of Vehicles(IoV)is an evolution of the Internet of Things(IoT)to improve the capabilities of vehicular ad-hoc networks(VANETs)in intelligence transport systems.The network topology in IoV paradigm is highly dynamic.Clustering is one of the promising solutions to maintain the route stability in the dynamic network.However,existing algorithms consume a considerable amount of time in the cluster head(CH)selection process.Thus,this study proposes a mobility aware dynamic clustering-based routing(MADCR)protocol in IoV to maximize the lifespan of networks and reduce the end-to-end delay of vehicles.The MADCR protocol consists of cluster formation and CH selection processes.A cluster is formed on the basis of Euclidean distance.The CH is then chosen using the mayfly optimization algorithm(MOA).The CH subsequently receives vehicle data and forwards such data to the Road Side Unit(RSU).The performance of the MADCR protocol is compared with that ofAnt Colony Optimization(ACO),Comprehensive Learning Particle Swarm Optimization(CLPSO),and Clustering Algorithm for Internet of Vehicles based on Dragonfly Optimizer(CAVDO).The proposed MADCR protocol decreases the end-toend delay by 5–80 ms and increases the packet delivery ratio by 5%–15%. 展开更多
关键词 clustering protocol internet of things internet of vehicles optimization algorithm Mayfly algorithm
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A Federated Bidirectional Connection Broad Learning Scheme for Secure Data Sharing in Internet of Vehicles 被引量:6
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作者 Xiaoming Yuan Jiahui Chen +2 位作者 Ning Zhang Xiaojie Fang Didi Liu 《China Communications》 SCIE CSCD 2021年第7期117-133,共17页
Data sharing in Internet of Vehicles(IoV)makes it possible to provide personalized services for users by service providers in Intelligent Transportation Systems(ITS).As IoV is a multi-user mobile scenario,the reliabil... Data sharing in Internet of Vehicles(IoV)makes it possible to provide personalized services for users by service providers in Intelligent Transportation Systems(ITS).As IoV is a multi-user mobile scenario,the reliability and efficiency of data sharing need to be further enhanced.Federated learning allows the server to exchange parameters without obtaining private data from clients so that the privacy is protected.Broad learning system is a novel artificial intelligence technology that can improve training efficiency of data set.Thus,we propose a federated bidirectional connection broad learning scheme(FeBBLS)to solve the data sharing issues.Firstly,we adopt the bidirectional connection broad learning system(BiBLS)model to train data set in vehicular nodes.The server aggregates the collected parameters of BiBLS from vehicular nodes through the federated broad learning system(FedBLS)algorithm.Moreover,we propose a clustering FedBLS algorithm to offload the data sharing into clusters for improving the aggregation capability of the model.Some simulation results show our scheme can improve the efficiency and prediction accuracy of data sharing and protect the privacy of data sharing. 展开更多
关键词 federated learning broad learning system deep learning internet of vehicles data privacy
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Privacy Protection Algorithm for the Internet of Vehicles Based on Local Differential Privacy and Game Model 被引量:5
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作者 Wenxi Han Mingzhi Cheng +3 位作者 Min Lei Hanwen Xu Yu Yang Lei Qian 《Computers, Materials & Continua》 SCIE EI 2020年第8期1025-1038,共14页
In recent years,with the continuous advancement of the intelligent process of the Internet of Vehicles(IoV),the problem of privacy leakage in IoV has become increasingly prominent.The research on the privacy protectio... In recent years,with the continuous advancement of the intelligent process of the Internet of Vehicles(IoV),the problem of privacy leakage in IoV has become increasingly prominent.The research on the privacy protection of the IoV has become the focus of the society.This paper analyzes the advantages and disadvantages of the existing location privacy protection system structure and algorithms,proposes a privacy protection system structure based on untrusted data collection server,and designs a vehicle location acquisition algorithm based on a local differential privacy and game model.The algorithm first meshes the road network space.Then,the dynamic game model is introduced into the game user location privacy protection model and the attacker location semantic inference model,thereby minimizing the possibility of exposing the regional semantic privacy of the k-location set while maximizing the availability of the service.On this basis,a statistical method is designed,which satisfies the local differential privacy of k-location sets and obtains unbiased estimation of traffic density in different regions.Finally,this paper verifies the algorithm based on the data set of mobile vehicles in Shanghai.The experimental results show that the algorithm can guarantee the user’s location privacy and location semantic privacy while satisfying the service quality requirements,and provide better privacy protection and service for the users of the IoV. 展开更多
关键词 The internet of vehicles privacy protection local differential privacy location semantic inference attack game theory
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A blockchain-based trustworthy collaborative power trading scheme for 5G-enabled social internet of vehicles 被引量:4
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作者 Ziming Liu Yang Xu +2 位作者 Cheng Zhang Haroon Elahi Xiaokang Zhou 《Digital Communications and Networks》 SCIE CSCD 2022年第6期976-983,共8页
Social Internet of Vehicles(SIoV)falls under the umbrella of social Internet of Things(IoT),where vehicles are socially connected to other vehicles and roadside units that can reliably share information and services w... Social Internet of Vehicles(SIoV)falls under the umbrella of social Internet of Things(IoT),where vehicles are socially connected to other vehicles and roadside units that can reliably share information and services with other social entities by leveraging the capabilities of 5G technology,which brings new opportunities and challenges,e.g.,collaborative power trading can address the mileage anxiety of electric vehicles.However,it relies on a trusted central party for scheduling,which introduces performance bottlenecks and cannot be set up in a distributed network,in addition,the lack of transparency in state-of-the-art Vehicle-to-Vehicle(V2V)power trading schemes can introduce further trust issues.In this paper,we propose a blockchain-based trustworthy collaborative power trading scheme for 5G-enabled social vehicular networks that uses a distributed market mechanism to introduce trusted power trading and avoids the dependence on a centralized dispatch center.Based on the game theory,we design the pricing and trading matching mechanism for V2V power trading to obtain maximum social welfare.We use blockchain to record power trading data for trusted pricing and use smart contracts for transaction matching.The simulation results verify the effectiveness of the proposed scheme in improving social welfare and reducing the load on the grid. 展开更多
关键词 Social internet of vehicles Bl ockchain Collaborative power trading Vehicle-to-vehicle charging 5G
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Reinforcement Learning Based Dynamic Spectrum Access in Cognitive Internet of Vehicles 被引量:3
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作者 Xin Liu Can Sun +2 位作者 Mu Zhou Bin Lin Yuto Lim 《China Communications》 SCIE CSCD 2021年第7期58-68,共11页
Cognitive Internet of Vehicles(CIoV)can improve spectrum utilization by accessing the spectrum licensed to primary user(PU)under the premise of not disturbing the PU’s transmissions.However,the traditional static spe... Cognitive Internet of Vehicles(CIoV)can improve spectrum utilization by accessing the spectrum licensed to primary user(PU)under the premise of not disturbing the PU’s transmissions.However,the traditional static spectrum access makes the CIoV unable to adapt to the various spectrum environments.In this paper,a reinforcement learning based dynamic spectrum access scheme is proposed to improve the transmission performance of the CIoV in the licensed spectrum,and avoid causing harmful interference to the PU.The frame structure of the CIoV is separated into sensing period and access period,whereby the CIoV can optimize the transmission parameters in the access period according to the spectrum decisions in the sensing period.Considering both detection probability and false alarm probability,a Q-learning based spectrum access algorithm is proposed for the CIoV to intelligently select the optimal channel,bandwidth and transmit power under the dynamic spectrum states and various spectrum sensing performance.The simulations have shown that compared with the traditional non-learning spectrum access algorithm,the proposed Q-learning algorithm can effectively improve the spectral efficiency and throughput of the CIoV as well as decrease the interference power to the PU. 展开更多
关键词 cognitive internet of vehicles reinforcement learning dynamic spectrum access Q-LEARNING spectral efficiency
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