基于动态图嵌入的车联网拓扑控制

针对由车联网的高度动态性和复杂性导致网络拓扑难以合理、稳定构建的问题,提出了关于车联网的范围标签图嵌入方法。首先,根据路侧单元分割车辆网络,利用驾驶员辅助系统获取实时车辆信息;其次,使用傅里叶变换、模糊推理对车辆信息预处理,获得车辆低维特征向量,再对新加入网络的车辆节点进行冷启动处理;最后,以所提动态图嵌入方法在区域内进行拓扑控制,充分利用车辆的特征信息构建车联网,实现网络的动态更新。实验结果表明,所提图嵌入方法建立的网络拓扑相对于传统网络及对比图嵌入方法,具有更好的动态性、连通性以及稳健性。     

车联网下基于网络编码的高吞吐量多径路由算法

在车联网中,由于车辆节点间无线链路的频繁中断易导致数据重传,使整个车辆组网性能急剧下降。为此,面向车联网提出了一种增强吞吐量的多径路由算法,其核心是使车辆节点分簇算法支持网络编码,从而达到对乱序和丢失报文恢复的目的。该算法首先采用协同编码通信模型来实现分簇后同簇车辆节点互相协同的多路径传输,然后采用网络编码对源端车辆和中间车辆节点发送的数据进行线性编码操作,最后在目的端节点予以解码。在基于QualNet仿真平台上的实验结果表明,该算法能够有效地提升车联网中多径路由的吞吐量。     

基于802.11p/WAVE的车联网连通性模型及其应用研究

首先对高速公路交通场景的车联网连通性模型进行了研究,分析推导了某特定路段上任意两车之间的连通概率、连通集直径长度以及连通集数目等连通性模型参数指标与车辆密度及传输距离之间关系的数学解析式,并在此基础上分析出车联网的节点位置是满足伽马分布的结论。接着根据车联网报文存储转发的特点,设计了一种车联网的报文格式,并应用连通性模型中的相关参数解析式给出了广播消息报文的TTL字段的初始值设定方案,从而能够有效地控制广播报文的泛滥情形,仿真实验证实了所建模工作的有效性,为车联网WAVE协议栈上层协议的设计提供了重要的理论基础。     

车联网关键技术及连通性研究

根据车联网的发展趋势,基于车联网的系统模型和关键技术,着重分析了车联网的连通性,具体研究了在车间通信中传输范围和可达邻接节点对连通性的影响。     

车联网中基于分簇结构的多人博弈路由模型的研究

车辆网中车辆的高速移动,网络拓扑频繁变化,运动轨迹多变等特性,为设计高效、可靠、实时的路由协议带来一定的挑战。通过研究和分析车联网环境下路由需要,综合节点间的信任和信号强度等考虑构建分簇结构。引入博弈论的合作与竞争理论对在簇内结点的关系进行分析,从簇内网络的负载均衡性、路由链路的稳定性等网络性能指标综合考虑,提出多人博弈算法构建路由模型。     

车联网的关键技术及应用研究﹡

车联网作为物联网的衍生品,在未来智能交通中将发挥极其重要的作用,是人们未来生活中不可缺少的一个重要组成部分.首先介绍了从物联网到车联网的发展过程,诠释了车联网的概念、分层的体系架构以及构成要素,通过与现有的智能交通系统、车载信息终端以及车辆自组织网络等几种车辆控制系统进行对比,分析了车联网独有的特点和优势所在,指出了车联网发展的几个关键技术,最后通过一个案例对车联网的应用进行了展望.     

基于802.11p协议的车联网传输策略研究

随着车辆普及度的提高,车联网技术应运而生。车辆自组织网络以IEEE 802.11p/1609.x协议为基础,使车与路边单元顺畅通信。车辆自组织网络为司机提供交通信息等,具有很大的实用价值。IEEE 802.11p协议是根据汽车通信环境设计的无线短距离通信标准,目前对其设定传输距离是否满足通信要求的研究较少。文章针对802.11p车联网传输协议研究问题进行评述,对车联网传输协议研究关键问题进行分析,指出车联网传输协议研究的切入点,有助于车联网技术走向实用。     

无人机辅助车联网的网络传输链路优化

无人机(UAV)通信技术的快速发展与智能车联网应用需求的极速增长促进了无人机辅助车联网系统的产生与发展。在无人机辅助车联网系统中,如何节省能量的同时最大化系统性能对于能量有限的节点十分重要。基于此,本文主要考虑无人机辅助车联网通信过程中如何选择最优的通信网络链路,从而最大化能量效率的问题。首先建立通信网络链路选择问题为混合整数规划问题,然后提出基于能量效率最大化的网络传输链路优化算法获得最优的传输链路及对应的能量分配,最后通过数值实验仿真验证了所提算法的有效性。     

激光通信网络下车联网车辆异常轨迹检测方法

为改善车联网车辆异常轨迹检测的精准度，避免车辆轨迹与路面信息不匹配而引发交通拥塞，本次提出基于激光通信网络研究车联网车辆异常轨迹检测方法。首先构建车联网车辆轨迹模型，分析车联网车辆轨迹特征；然后分析车联网激光通信信道的均衡情况，采用均衡技术完成车联网信道均衡配置；最后根据异常信号反馈结果，实现车联网车辆的异常轨迹检测。实验结果表明，该方法能有效实现车联网车辆的异常轨迹识别，其轨迹扩散度为0.541。平均误转发率为0.041,保真度较高，具有较好的应用价值。     

车联网相关感知技术、产品以及案例分析

车辆感知技术是车联网的末梢神经,是车联网最关键的技术。车联网感知技术分为车况及控制系统感知和道路环境感知。介绍了车联网感知系列的电子产品,以及国内外车辆感知技术发展现状。最后介绍了车联网感知技术的应用案例,梅赛德斯—奔驰公司开发的循迹辅助系统。     

A Dual Ring Connectivity Model for VANET Under Heterogeneous Traffic Flow

Vehicular ad-hoc network (VANET) is characterized as a highly dynamic wireless network due to the dynamic connectivity of the network nodes. To achieve better connectivity under such dynamic conditions, an optimal transmission strategy is required to direct the information flow between the nodes. Earlier studies on VANET’s overlook the characteristics of heterogeneity in vehicle types, traffic structure, flow for density estimation, and connectivity observation. In this paper, we have proposed a heterogeneous traffic flow based dual ring connectivity model to enhance both the message disseminations and network connectivity. In our proposed model the availability of different types of vehicles on the road, such as, cars, buses, etc., are introduced in an attempt to propose a new communication structure for moving vehicles in VANETl under cooperative transmission in heterogeneous traffic flow. The model is based on the dual-ring structure that forms the primary and secondary rings of vehicular communication. During message disseminations, Slow speed vehicles (buses) on the secondary ring provide a backup path of communication for high speed vehicles (cars) moving on the primary ring. The Slow speed vehicles act as the intermediate nodes in the aforementioned connectivity model that helps improve the network coverage and end-to-end data delivery. For the evaluation and the implementation of dual-ring model a clustering routing scheme warning energy aware cluster-head is adopted that also caters for the energy optimization. The implemented dual-ring message delivery scheme under the cluster-head based routing technique does show improved network coverage and connectivity dynamics even under the multi-hop communication system.     

Data-centric approaches in the Internet of Vehicles: A systematic review on techniques,open issues,and future directions

The Internet of Vehicles (IoV) is an emerging network of connected vehicles as a branch of dynamic objects in the Internet of Things (IoT) ecosystem. With the rapid development of IoV, real-time data-centric applications would be a significant concern in academia and industry to promote efficiency and realize modern services in such high dynamic networks. In this paper, we aim to present a systematic literature review (SLR) for the IoV networks to investigate the different attitudes in the field of data-centric approaches. This paper systematically categorizes the 48 recent articles on data-driven techniques in the IoV field published from 2017 to March 2022. A complete technical taxonomy is presented for the data-centric approaches in IoV according to the content of current studies. Collected methods are chosen with the SLR process, and they are investigated considering some technical classifications including IoV security, data traffic, vehicular social network, data propagation, energy, and multimedia categories. The achievements, drawbacks, and new findings of studies are carefully investigated for addressing the deficiencies, as well as emphasizing future research direction and open issues of data-driven approaches in IoV.     

远程遥控驾驶场景下基于网络信息开放的QoS预测

5G系统的飞速发展支持许多车联网用例对服务质量（quality of service,Qo S）的苛刻要求。但网络和应用的适配仍然存在很多问题。网络信息开放是一种潜在的解决方案,旨在实现网络向应用程序实时提供蜂窝无线网络信息,从而帮助服务提供方实现更好的策略控制并改善用户体验。提出一种基于网络信息开放的服务质量预测（predictive QoS,PQo S）方法,通过提前预测即将发生的网络变化来支持应用做出提前响应,提高用户的体验质量（qualityofexperience,Qo E）。介绍了网络信息开放及PQo S的背景,并介绍了PQo S的国内外研究、标准及落地现状;提出一种远程遥控驾驶（tele-operateddriving,To D）场景下基于网络信息开放的Qo S预测方法;对实际测试的数据进行分析,评估验证了PQo S的可行性。结果表明,基于网络信息开放的Qo S预测技术能够良好地支持包括5G ToD在内的车联网应用,为5G系统在智慧交通行业的落地提供了参考。     

Connectivity analysis for dynamic movement of vehicular ad hoc networks

Speed variation is one of the main challenges in deriving the connectivity related predictions in mobile ad-hoc networks, especially in vehicular ad hoc networks (VANETs). In such a dynamic network, a piece of information can be rapidly propagated through dedicated short-range communication, or can be carried by vehicles when multihop connectivity is unavailable. This paper proposes a novel analytical model that carefully computes the connectivity distance for a single direction of a free-flow highway. The proposed model adopts a time-varying vehicular speed assumption and mathematically models the mobility of vehicles inside connectivity. According to the dynamic movability scenario, a novel and accurate closed form formula is proposed for probability density function of connectivity. Moreover, using vehicular spatial distribution, joint Poisson distribution of vehicles in a multilane highway and tail probability of the expected number of vehicles inside single lane in a multilane highway are mathematically investigated. The accuracy of analytical results is verified by simulation. The concluded results provide helpful insights towards designing new applications and improving performance of existing applications on VANETs.     

Candidate architectures for emerging IoV: a survey and comparative study

Intelligent Transportation System (ITS) is observing significant evolution in terms of technology and investment worldwide. This has given birth to the new concept of Internet of vehicles (IoV) as one of the leading applications of the Internet of Things. IoV aims to offer a better sharing of information and communication between vehicles, enabling higher cooperation for common interests. IoV is increasingly attracting the interest of a significant body of research. The e ort was mostly focused on solving various problems encountered in traditional VANETs, such as lack of coordination between vehicles, insufficient information, scalability, etc. Rapidly, IoV observed, particularly interesting advances taking advantage of exponential growth in communication and data analysis technologies. This includes cloud and/or fog computing, large data analytics, machine learning, and artificial intelligence. In this paper, we make a survey of the existing and recently proposed architecture solutions for IoV systems. Moreover, we define a list of criteria, features, and properties associated to the various architectures in order of making critical and insightful comparisons and assessments. Finally, we outline the key future research perspectives on the topic and define the key technical aspects that will help drive the future of IoV architectures.

     

Dynamic Mix-zone scheme with joint-entropy based metric for privacy-perserving in IoV

Aiming at the weak flexibility and lack of users’ transparency existing in the current Mix-zone schemes for Internet of vehicle (IoV),a dynamic was proposed for Mix-zone construction with traffic adaption,which could construct a Mix-zone for the vehicles dynamically according to the traffic conditions for changing pseudonym at anytime and anywhere.This kind of Mix-zone could achieve privacy-preserving based on the identity and location.In addition,a novel traffic-adaptive metric was presented for classifying the privacy leveled in Mix-zone,which applied the normalization quantitation to measure the degree of Mix-zone’s privacy demanding by the current region.It was verified that the joint entropy-based privacy measuring model and the Mix-zone construction scheme by utilizing the trajectory data of taxis in certain district in Shenzhen city.The experimental shows that the proposed combination entropy-based model could depict the proportional relationship between the traffic scene parameters and the privacy-preserving degrees.The scheme is better in performance over the related methods,and strikes a good balance between location privacy and service usability.     

Research on optimal intelligent routing algorithm for IoV with machine learning and smart contract

The huge increase in the communication network rate has made the application fields and scenarios for vehicular ad hoc networks more abundant and diversified and proposed more requirements for the efficiency and quality of data transmission. To improve the limited communication distance and poor communication quality of the Internet of Vehicles (IoV), an optimal intelligent routing algorithm is proposed in this paper. Combined multi-weight decision algorithm with the greedy perimeter stateless routing protocol, designed and evaluated standardized function for link stability. Linear additive weighting is used to optimize link stability and distance to improve the packet delivery rate of the IoV. The blockchain system is used as the storage structure for relay data, and the smart contract incentive algorithm based on machine learning is used to encourage relay vehicles to provide more communication bandwidth for data packet transmission. The proposed scheme is simulated and analyzed under different scenarios and different parameters. The experimental results demonstrate that the proposed scheme can effectively reduce the packet loss rate and improve system performance.     

Survey on IoV routing protocols: Security and network architecture

As today, vehicles are equipped with wireless sensors and on‐board computers capable of collecting and processing a large amount of data; they can communicate to each other via different communication types and through different relay nodes. Internet of Vehicles (IoV) routing protocols are deployed to monitor these communications with various strategies to achieve a high availability of communication. In this paper, we propose to extend an existing taxonomy representing the necessary criteria to build IoV routing algorithms, by adding two new important criteria: security aspect and network architecture. Enhanced vehicular routing protocols with different security mechanisms have been studied, compared, and classified with respect to the authentication, the integrity, the confidentiality, the nonrepudiation, and the availability of data and communications. Routing protocols using the software‐defined networking (SDN) paradigm have also been reviewed in order to compare with those with traditional network architectures. Three types of SDN routing protocols, namely, centralized, decentralized, and hybrid control planes, have been analyzed. This survey will be useful for the choice of IoV routing protocols that take into account the flexibility, the scalability, and the intelligence of vehicular networks, as well as the security mechanisms against cyberattacks while being cost aware.     

An efficient task offloading strategy based on Aquila Student Psychology Optimization Algorithm in internet of vehicles-fog computing systems

Internet of vehicles (IoV) comprises connected vehicles and connected autonomous vehicles and offers numerous benefits for ensuring traffic and safety competence. Several IoV applications are delay-sensitive and need resources for computation and data storage that are not provided by vehicles. Therefore, these tasks are always offloaded to highly powerful nodes, namely, fog, which can bring resources nearer to the networking edges, reducing both traffic congestion and load. Besides, the mechanism of offloading the tasks to the fog nodes in terms of delay, computing power, and completion time remains still as an open concern. Hence, an efficient task offloading strategy, named Aquila Student Psychology Optimization Algorithm (ASPOA), is developed for offloading the IoV tasks in a fog setting in terms of the objectives, such as delay, computing power, and completion time. The devised optimization algorithm, known as ASPOA, is the incorporation of Aquila Optimizer (AO) and Student Psychology Based Optimization (SPBO). Task offloading in the IoV-fog system selects suitable resources for executing the tasks of the vehicles by considering several constraints and parameters to satisfy the user requirements. The simulation outcomes have shown that the devised ASPOA-based task offloading method has achieved better performance by achieving a minimum delay of 0.0009 s, minimum computing power of 8.884 W, and minimum completion time of 0.441 s.