面向协同驾驶基于移动边缘计算的5G智能网联车辆服务平台

基于5G通信技术的发展,5G网络开始普及,并与物联网技术、汽车信息通信、人工智能技术等进行了有机融合,促使车联网行业走向智能化发展。截至2021年,我国车联网行业的标准体系基本建立。文中以5G智能网联车辆服务平台为研究对象,分析了移动边缘计算技术的应用,研究了5G通信技术在智能网联车辆服务平台中的全面应用,实现了车辆智能化编队、车辆行驶环境感应、车辆行驶资源分配、车辆安全预警4种核心功能。     

车联网中基于分布式博弈的移动网关选取算法

提出一种基于VANET-CELLULAR混合架构的车联网(VANET)。在这种架构下,车辆可以根据一些原则动态成簇,在这些簇中,通过车辆间分布式博弈快速选取最少数量的车辆作为移动网关,将本地VANET接入蜂窝网络,并随着簇的网络拓扑变化动态更新网关,以最大化车辆数据传输的性能。考虑到车辆节点的自私特点,分布式博弈可以有效地提高移动网关节点的积极性,使得算法的可行性增强。仿真结果表明该算法能高效选取车联网移动网关。     

基于移动Agent的JPEG2000分布式编码算法研究

该文针对无线传感器网络节点处理能力、存储能力和能量供应均有限的特点,提出一种基于移动Agent的JPEG2000分布式编码算法。采用多个节点对图像进行分布式编码,通过引入移动Agent机制来实现网络节点间的信息交互,在保证图像编码性能不变的情况下,降低单个节点能耗,使系统不因单个节点能量耗尽而瘫痪,以延长系统的生命周期。仿真实验结果表明,该文所提出的基于移动Agent的分布式编码算法在无线传感器网络环境中可以保证编码后的图像质量没有下降,并能有效均衡系统能耗、延长网络工作寿命达3倍左右。     

车联网中整合移动边缘计算与内容分发网络的移动性管理策略

由于车载应用的普及和车辆数量的增加,路边基础设施的物理资源有限,当大量车辆接入车联网时能耗与时延同时增加,通过整合内容分发网络(CDN)和移动边缘计算(MEC)的框架可以降低时延与能耗。在车联网中,车辆移动性对云服务的连续性提出了重大挑战。因此,该文提出了移动性管理(MM)来处理该问题。采用开销选择的动态信道分配(ODCA)算法避免乒乓效应且减少车辆在小区间的切换时间。采用基于路边单元(RSU)调度的合作博弈算法进行虚拟机迁移并开发基于学习的价格控制机制,以有效地处理MEC的计算资源。仿真结果表明,所提算法相比于现有的算法能够提高资源利用率且减少开销。

     

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。     

基于北斗和边缘计算的车联网导航技术研究

随着车辆保有量的不断增长和车联网应用的普及,车辆终端会产生大量需要实时处理的数据消息。在车辆高速移动场景下,传统的车联网导航系统由于车辆差分定位数据存在传输时延,导致车辆定位结果存在一定的偏差,无法及时获得高精度定位结果。基于此,文中提出了一种基于北斗定位和边缘计算的车联网导航技术方案,采用改进的遗传算法进行终端定位请求的资源分配,有效降低整个边缘网络的服务时延,并利用基于边缘节点的优化无损卡尔曼滤波算法来提高车联网节点的定位精度。实验表明,文中所提出的方法能够为大规模车联网终端提供实时精准、低延迟和高精度的定位服务,具有较高的实际应用价值。     

分布式智能车载网联系统的匿名认证与密钥协商协议

智能车载网联系统作为智慧城市建设的重要组成部分,近年来受到学术界与工业界越来越多的关注。智能车载网联系统中提升了智能车辆的行驶安全性与出行效率,但在开放的环境下数据传输容易被截取,造成敏感信息泄漏。因此需要实现匿名认证并且协商正确的会话密钥,来确保智能车载网联系统敏感信息的安全。该文提出面向分布式智能车载网联系统架构的匿名认证与密钥协商协议。该协议基于中国剩余定理秘密分享技术来保护认证标识符,智能车辆能够以线性的计算开销在不同的区域恢复出对应的标识符,该标识符能够长期安全使用且智能车辆能够在不使用防篡改设备的情况下完成安全认证,路侧通信基站能够检测信息的匿名性和完整性,并与智能车辆协商到后续安全通信的会话密钥,同时实现双向认证。此外,协议能够在复杂的分布式智能车载网联系统中拓展批量匿名认证、域密钥更新、车对车的匿名认证、匿名身份可追踪等实用性功能。安全性与性能分析表明该协议能够安全高效地部署在分布式智能车载网联环境。     

一种基于运动搜索的运动检测和边缘检测算法

提出了一种基于运动搜索的运动检测和边缘检测算法并估计了其硬件消耗.该算法利用视频编码器在编码过程中的中间过程计算数据,相对传统的视频编码器,以很少的电路结构消耗实现了运动检测和边缘检测两项功能.试验结果表明,提出的算法可快速、有效地获得满意的运动检测和边缘检测结果,并具有较强的鲁棒性.     

车联网卡实名登记助力车联网网络安全

<正>随着汽车产业向智能化、网联化、数据化发展,我国车联网产业进入高速发展阶段,车辆也正在从传统的交通运输工具转变为新型的智能出行载体。有关数据显示,目前我国搭载智能网联功能的新车渗透率已超过50%。作为一个移动的智能载体,车辆可收集大量的用户信息、车辆行驶信息、道路信息、环境信息等,并通过车辆上装载的车联网卡实现“人、车、路、云”之间的数据互通,最终实现智能车辆、车联网。虽然车联网为人们生活带来了便利,但同时也带来了远程攻击、恶意控制、隐私保护、数据安全等问题。车联网的安全不仅关乎到用户隐私安全,还可能影响人身安全,甚至引发社会安全问题。     

面向边缘智能的车联网通信和计算资源联合管理策略

海量数据驱动未来车联网向智能化演进,计算密集型业务的激增给网络中通信和计算资源的管理带来了极大的挑战。为了解决上述问题,提出了面向边缘智能的车联网通信和计算资源联合管理策略,在考虑各边缘节点内存容量的前提下,通过模型切分将适量计算任务卸载到最优边缘节点,提高任务执行率并降低系统能耗。上述资源联合管理策略可建模为动态优化问题,传统的优化方法难以求解。因此,将人工智能技术应用到边缘计算领域,采用多智能体深度强化学习方法合理分配网络频谱资源和计算资源,提升网络性能。     

Machine learning in vehicular networking: An overview

As vehicle complexity and road congestion increase, combined with the emergence of electric vehicles, the need for intelligent transportation systems to improve on-road safety and transportation efficiency using vehicular networks has become essential. The evolution of high mobility wireless networks will provide improved support for connected vehicles through highly dynamic heterogeneous networks. Particularly, 5G deployment introduces new features and technologies that enable operators to capitalize on emerging infrastructure capabilities. Machine Learning (ML), a powerful methodology for adaptive and predictive system development, has emerged in both vehicular and conventional wireless networks. Adopting data-centric methods enables ML to address highly dynamic vehicular network issues faced by conventional solutions, such as traditional control loop design and optimization techniques. This article provides a short survey of ML applications in vehicular networks from the networking aspect. Research topics covered in this article include network control containing handover management and routing decision making, resource management, and energy efficiency in vehicular networks. The findings of this paper suggest more attention should be paid to network forming/deforming decision making. ML applications in vehicular networks should focus on researching multi-agent cooperated oriented methods and overall complexity reduction while utilizing enabling technologies, such as mobile edge computing for real-world deployment. Research datasets, simulation environment standardization, and method interpretability also require more research attention.     

基于车辆行为分析的智能车联网关键技术研究

车联网通信系统中通信节点的高移动性、移动行为的复杂性,使得此场景下通信业务呈现数据实时交互性强、空时分布不均、尺度多变、规律复杂的特征,导致传统的车联网网络部署、资源调配难以有效满足用户的差异化服务质量需求。因此,迫切需要设计“车-人-路-云”泛在互联的智能异构车联网网络,通过充分挖掘车辆行为数据的潜在价值,精准预测、刻画车辆行为的空时分布特性,以提升车联网资源利用率、改善车联网服务性能。该文全面梳理了国内外在车辆行为分析、网络部署与接入以及资源优化方面的相关工作,重点阐述了智能车联网关键使能技术,即如何借助先进的人工智能、数据分析技术,探索车联网中车辆行为的空时分布特性,建立车辆行为预测模型,进行智能化网络部署与多网接入、动态资源优化管理,实现高容量、高效率的智能车联网通信。     

Dependable and reliable cloud-based architectures for vehicular communications: A systematic literature review

Traffic congestion, air pollution, fuel wastage, and car accidents are all exacerbated by increased traffic. Thus, vehicular communications, which refer to information transmission between cars, pedestrians, and infrastructures, have lately gained popularity and been extensively explored due to their enormous potential to enable intelligent transportation and various safety applications. Manually piloted cars and automated vehicles can acquire relevant information via vehicular communications to enhance traffic security and boost entertainment services. The basic concept of automobile clouds was originally published in the literature not long ago, and several suggested structural approaches have been presented in this study thus far. Several academics have concentrated on the structural layout to address various problems and, as a result, satisfy user expectations in order to give dependable services. We examined various vehicular cloud topologies in this study. We also offered a complete summary of current network layer research on allowing efficient vehicle communications and examined specific security, architectural, and reliability concerns in vehicular clouds. Also, the taxonomy of vehicular networks was discussed in terms of the service link between vehicular networks and cloud computing. Ultimately, we discussed the research prospects available. The results showed that security and privacy challenges are among the most important challenges.     

An evolutionary game theory–based security model in vehicular ad hoc networks

The word population is growing on a daily basis; consequently, the growth of commute and transport, developing efficient and intelligent transportation systems (ITS), has become one of the most popular requirements and the most significant attempts in modern urban areas containing large population. A key component of intelligent transportation systems is a vehicular ad hoc network. Devising internet‐based practical programs such as awareness of climatic conditions, geographical location, practical programs like on‐line payment services in the vehicular ad hoc network has led to safer driving, prevention of deadly accidents, transportation improvement, more welfare and convenience for passengers, and even offering more commercial opportunities. The special features of vehicular ad hoc network, such as intense activity, constantly‐changing topology, the vehicles' high speed, etc, will lead to challenges in gaining security. Therefore, providing vehicular ad hoc networks with security is of extreme importance in terms of users' anonymity, identification, and data privacy. In this paper, a security model is presented using a method based on evolutionary games. This method, in every vehicle, is applied as a node in the network while interacting with other vehicles; it aims at distinguishing some common attacks and defending against attackers. In this method, defending (honest) vehicles and attacking vehicles take part in an asymmetrical game; each vehicle aims at gaining the most utility and achieving its goals. The proposed method is simulated using various scenarios. The simulation results reveal that the proposed method is efficient and it reaches equilibrium and convergence at the end of the game in each scenario.     

智能网联汽车的安全风险及对策

智能网联汽车集人工智能、车联网、大数据、移动互联等新技术于一身,给人们带来了许多传统汽车无法提供的新功能,让用户获得了比传统汽车更智能、舒适、便捷的用车体验。但是,智能网联汽车需要实时联网,深度依赖网络,存在诸多安全风险,需要予以重视。文中分析了智能网联汽车的安全风险,并提出了制定智能网联汽车信息安全标准、构建智能网联汽车数据安全防护体系、建立常态化的风险评估机制、提升车企技术水平、应用标识密钥技术、确保数据跨境流动安全等对策,以提升智能网联汽车的安全性。     

车载边缘计算中任务卸载和服务缓存的联合智能优化

针对车载环境下有限的网络资源和大量用户需求之间的矛盾,提出了智能驱动的车载边缘计算网络架构,以实现网络资源的全面协同和智能管理.基于该架构,设计了任务卸载和服务缓存的联合优化机制,对用户任务卸载以及计算和缓存资源的调度进行了建模.鉴于车载网络的动态、随机和时变的特性,利用异步分布式强化学习算法,给出了最优的卸载决策和资...     

Security in service-oriented vehicular networks - [Service-oriented broadband wireless network architecture]

Service-oriented vehicular networks support diverse infrastructure-based commercial services including Internet access, real-time traffic concerns, video streaming, and content distribution. The success of service delivery in vehicular networks depends on the underlying communication system to enable the user devices to connect to a large number of communicating peers and even to the Internet. This poses many new research challenges, especially in the aspects of security, user privacy, and billing. In this article we first identify the key requirements of authentication, privacy preservation, and billing for service delivery in vehicular networks. We then review the existing industrial and academic efforts on service- oriented vehicular networks. We also point out two security challenges, minimizing vehicleto- infrastructure authentication latency and distributed public key revocation, which are considered among the most challenging design objectives in service-oriented vehicular networks. A novel fast vehicle-to-infrastructure authentication based on a vehicle mobility prediction scheme and an infrastructure-based short-time certificate management scheme are then proposed to address these two challenges.     

A buffer‐aware fleet‐based cooperative H.264/SVC streaming over vehicular networks

Because of of the characteristics of high mobility, time varying and dynamic topology, how to provide multimedia streaming service for connected vehicles becomes one emerging and popular technical research. The motivation of this paper is to utilize cooperation among neighboring vehicles for video streaming's quality improvement over vehicular networks. In the proposed cooperative streaming scenario, a connected vehicle requests a video stream from the Internet by using its 3G/3.5G interface, which may not have enough bandwidth to receive good quality of video. Thus, the vehicle is suggested to ask neighboring members belonging to the same fleet to download the requested video data by using their 3G/3.5G interfaces. Then, neighboring members should forward video data to the requested vehicle by using another wireless technique, for example, dedicated short range communication (DSRC). Regarding the differentiation between the two access networks, that is, 3G/3.5G network and DSRC network, a buffer‐aware scheduling mechanism based on layered streaming is designed in this paper to adapt to the networking situation of the vehicular networks. Two selection algorithms are proposed to select neighboring vehicles from the fleet. According to our simulation results, the 3G/3.5G‐based selection algorithm is suitable to improve video quality for vehicles at low speeds. On the other hand, the DSRC‐based selection algorithm can get better performance when vehicles move at high speeds or too many data are transmitted among vehicles.Copyright © 2013 John Wiley & Sons, Ltd.     

区块链赋能的6G零信任车联网可信接入方案

6G网络将带来全场景按需服务泛在智能新范式,其中可信可靠网络服务是泛在智能的关键技术指标。该文面向6G零信任网络的通信需求,以区块链为“信任桥梁”,研究6G车联网边缘计算中的可信可靠接入管理方法。首先,采用基于2次剩余的零知识身份验证算法,在不暴露车辆隐私的前提下完成基站和车辆之间的相互验证与授权。然后,为提高验证效率并节省基站能耗,建立了基于契约理论的路侧冗余算力激励模型,将基站的一部分验证任务分配给边缘服务器或停泊车辆,再给予相应的报酬。最后,建立了基于双层区块链的6G零信任车联网架构,利用基站群维护的主链与边缘算力维护的辅链记录车联网身份验证的重要参数,实现零信任网络环境的可信接入。通过与现有方法比较,该文所述方法在不泄露车辆隐私的前提下显著提升了车辆验证效率,降低了基站能耗,具有更高的安全性。     

Task offloading under deterministic demand for vehicular edge computing

In vehicular edge computing (VEC) networks, the rapid expansion of intelligent transportation and the corresponding enormous numbers of tasks bring stringent requirements on timely task offloading. However, many tasks typically appear within a short period rather than arriving simultaneously, which makes it difficult to realize effective and efficient resource scheduling. In addition, some key information about tasks could be learned due to the regular data collection and uploading processes of sensors, which may contribute to developing effective offloading strategies. Thus, in this paper, we propose a model that considers the deterministic demand of multiple tasks. It is possible to generate effective resource reservations or early preparation decisions in offloading strategies if some feature information of the deterministic demand can be obtained in advance. We formulate our scenario as a 0-1 programming problem to minimize the average delay of tasks and transform it into a convex form. Finally, we proposed an efficient optimal offloading algorithm that uses the interior point method. Simulation results demonstrate that the proposed algorithm has great advantages in optimizing offloading utility.