3D Road Network Modeling and Road Structure Recognition in Internet of Vehicles

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.

Traffic Management in Internet of Vehicles Using Improved Ant Colony Optimization

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.

Container-Based Internet of Vehicles Edge Application Migration Mechanism

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.

SDN-Enabled Content Dissemination Scheme for the Internet of Vehicles

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.

Federated Learning with Blockchain for Privacy-Preserving Data Sharing in Internet of Vehicles

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.

Communication-Efficient Decision-Making of Digital Twin Assisted Internet of Vehicles: A Hierarchical Multi-Agent Reinforcement Learning Approach

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.

PGSLM:Edge-Enabled Probabilistic Graph Structure Learning Model for Traffic Forecasting in Internet of Vehicles

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.

Security and Privacy in 5G Internet of Vehicles(IoV)Environment

Modern vehicles are equipped with sensors,communication,and computation units that make them capable of providing monitoring services and analysis of real-time traffic information to improve road safety.The main aim of communication in vehicular networks is to achieve an autonomous driving environment that is accident-free alongside increasing road use quality.However,the demanding specifications such as high data rate,low latency,and high reliability in vehicular networks make 5G an emerging solution for addressing the current vehicular network challenges.In the 5G IoV environment,various technologies and models are deployed,making the environment open to attacks such as Sybil,Denial of Service(DoS)and jamming.This paper presents the security and privacy challenges in an IoV 5G environment.Different categories of vehicular network attacks and possible solutions are presented from the technical point of view.

Internet of Vehicles in Big Data Era

As the rapid development of automotive telematics,modern vehicles are expected to be connected through heterogeneous radio access technologies and are able to exchange massive information with their surrounding environment. By significantly expanding the network scale and conducting both real-time and long-term information processing, the traditional Vehicular AdHoc Networks(VANETs) are evolving to the Internet of Vehicles(Io V), which promises efficient and intelligent prospect for the future transportation system. On the other hand, vehicles are not only consuming but also generating a huge amount and enormous types of data, which is referred to as Big Data. In this article, we first investigate the relationship between Io V and big data in vehicular environment, mainly on how Io V supports the transmission, storage, computing of the big data, and how Io V benefits from big data in terms of Io V characterization,performance evaluation and big data assisted communication protocol design. We then investigate the application of Io V big data in autonomous vehicles. Finally, the emerging issues of the big data enabled Io V are discussed.

A Deep Learning Based Energy-Efficient Computational Offloading Method in Internet of Vehicles

With the emergence of advanced vehicular applications, the challenge of satisfying computational and communication demands of vehicles has become increasingly prominent. Fog computing is a potential solution to improve advanced vehicular services by enabling computational offloading at the edge of network. In this paper, we propose a fog-cloud computational offloading algorithm in Internet of Vehicles(IoV) to both minimize the power consumption of vehicles and that of the computational facilities. First, we establish the system model, and then formulate the offloading problem as an optimization problem, which is NP-hard. After that, we propose a heuristic algorithm to solve the offloading problem gradually. Specifically, we design a predictive combination transmission mode for vehicles, and establish a deep learning model for computational facilities to obtain the optimal workload allocation. Simulation results demonstrate the superiority of our algorithm in energy efficiency and network latency.

A Novel Load Balancing Strategy of Software-Defined Cloud/Fog Networking in the Internet of Vehicles

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.

An Overview of Internet of Vehicles

The new era of the Internet of Things is driving the evolution of conventional Vehicle Ad-hoc Networks into the Internet of Vehicles(IoV).With the rapid development of computation and communication technologies,IoV 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 IoV.

MADCR:Mobility Aware Dynamic Clustering-Based Routing Protocol in Internet of Vehicles

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%.

Optimization of the Internet of Remote Things Data Acquisition Based on Satellite UAV Integrated Network

In order to achieve dependable and efficient data acquisition and transmission in the Internet of Remote Things(IoRT),we investigate the optimization scheme of IoRT data acquisition under the unmanned aerial vehicle(UAV)-low earth orbit(LEO)satellite integrated space-air-ground network,in which the UAV acquires data from massive Internet of Things(IoT)devices in special scenarios.To combine with the actual scenario,we consider two different data types,that is,delay-sensitive data and delay-tolerant data,the transmission mode is accordingly divided into two types.For delay-sensitive data,the data will be transmitted via the LEO satellite relay to the data center(DC)in real-time.For delay-tolerant data,the UAV will store and carry the data until the acquisition is completed,and then return to DC.Due to nonconvexity and complexity of the formulated problem,a multi-dimensional optimization Rate Demand based Joint Optimization(RDJO)algorithm is proposed.The algorithm first uses successive convex approximation(SCA)technology to solve the non-convexity,and then based on the block coordinate descent(BCD)method,the data acquisition efficiency is maximized by jointly optimizing UAV deployment,the bandwidth allocation of IoRT devices,and the transmission power of the UAV.Finally,the proposed RDJO algorithm is compared with the conventional algorithms.Simulation consequences demonstrate that the efficiency of IoRT data acquisition can be greatly improved by multi-parameter optimization of the bandwidth allocation,UAV deployment and the transmission power.

Reinforcement Learning Based Dynamic Spectrum Access in Cognitive Internet of Vehicles

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.

The Innovation and Development of Internet of Vehicles

With the advancements in wireless sensor networks, Internet of Vehicles(IOV) has shown great potential in aiding to ease traffic congestion. In IOV, vehicles can easily exchange information with other vehicles and infrastructures, thus, the development of IOV will greatly improve vehicles safety, promote green information consumption and have a profound impact on many industries. The purpose of this paper is to promote the innovation and development of IOV. Firstly, this paper presents general requirements of IOV such as guidelines, basic principles, and the goal of development. Secondly, we analyze critical applications, crucial support, and business model to promote the industrial development of IOV. Finally, this paper proposes some safeguard measures to further promote the development of IOV.

A Federated Bidirectional Connection Broad Learning Scheme for Secure Data Sharing in Internet of Vehicles

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.

Privacy Protection Algorithm for the Internet of Vehicles Based on Local Differential Privacy and Game Model

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.

A Reliable Routing Algorithm with Network Coding in Internet of Vehicles

The intelligent vehicle network uses advanced information technology to establish an efficient integrated vehicle transport system,which has received great attention in industry and academia. Internet of Vehicles(IoV)in an urban environment is operated in a wireless environment with high bit error rate and interference. In addition,the wireless link between vehicles is likely to be lost. All of this makes it an important challenge to provide reliable mobile routing in an urban traffic environment. In this paper,a reliable routing algorithm with network coding(RR_NC)is proposed to solve the above problems. A routing node sequence is discovered in IoV from source to destination by multi-metric ant colony optimization algorithm(MACO),and then clusters are formed around every node in the sequence. By adding linear encoding into the transmission of data between vehicle's clusters,the RR_NC provides much more reliable transmission and can recover the original message in the event of disorder and loss of message. Simulations are taken under different scenarios,and the results prove that this novel algorithm can deliver the information more reliably between vehicles in real-time with lower data loss and communication overhead.

A blockchain-based trustworthy collaborative power trading scheme for 5G-enabled social internet of vehicles

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.