Design and performance analysis of an inductive QoS routing algorithm

Routing mechanism is key to the success of large-scale, distributed communication and heterogeneous networks. Consequently, computing constrained shortest paths is fundamental to some important network functions such as QoS routing and traffic engineering. The problem of QoS routing with multiple additive constraints is known to be NP-complete but researchers have been designing heuristics and approximation algorithms for multi-constrained paths algorithms to propose pseudo-polynomial time algorithms. This paper introduces a polynomial time approximation quality of service (QoS) routing algorithm and constructs dynamic state-dependent routing policies. The proposed algorithm uses an inductive approach based on trial/error paradigm combined with swarm adaptive approaches to optimize lexicographically various QoS criteria. The originality of our approach is based on the fact that our system is capable to take into account the dynamics of the network where no model of the network dynamics is assumed initially. Our approach samples, estimates, and builds the model of pertinent aspects of the environment which is very important in heterogeneous networks. The algorithm uses a model that combines both a stochastic planned pre-navigation for the exploration phase and a deterministic approach for the backward phase. Multiple paths are searched in parallel to find the K best qualified ones. To improve the overall network performance, a load adaptive balancing policy is defined and depends on a dynamic traffic path probability distribution function. We conducted a performance analysis of the proposed QoS routing algorithm using OPNET based on a platform simulated network. The obtained results demonstrate substantial performance improvements as well as the benefits of learning approaches over networks with dynamically changing traffic.     

Peer-to-peer data structures for cooperative traffic information systems

Cooperative traffic information systems support the driver of a car in selecting a route, based on traffic information collected by other cars. We propose to use a peer-to-peer network based on Internet access via cellular networks to distribute traffic information between the participants of such a system. This approach avoids the well-known limitations of VANET-based communication. Since the data maintained in a cooperative traffic information system has a very specific structure, it is particularly profitable—in terms of bandwidth consumption and latency—to tailor the system to this specific application domain instead of re-using generic peer-to-peer approaches. This realization led us to the development of GraphTIS—a peer-to-peer network specifically designed to manage traffic information. In this paper, we derive, step-by-step, the core mechanisms of GraphTIS, starting with a standard peer-to-peer system, outlining a first solution—named PeerTIS—which is based on a modification of this standard DHT, and then presenting GraphTIS, a novel peer-to-peer system that has been specifically designed to support traffic information systems.     

CATS: An adaptive traffic signal system based on car-to-car communication

Traffic signal controls play an important role in regulating vehicular flow at road intersections. Traditional systems are not capable of adjusting the timing pattern in accordance with vehicular demand. This results in excessive delays for road users. Hence it is necessary to develop dynamic systems that can adjust the timing patterns according to traffic demand. In this paper, the design and implementation of an adaptive traffic signal control system based on car-to-car communication is presented. Also, a clustering algorithm is defined which will assist in estimating the density of vehicles approaching an intersection. The cycle time, which is calculated using the estimated density of vehicular traffic, helps in reducing both the waiting time for vehicles at intersections and queue length. It is also shown that the proposed solution is collision free at intersections. The proposed system is compared with a classic pre-timed system and an adaptive fuzzy logic system. The simulations also show that the data convergence time and the communication delay between vehicles and traffic signals do not compromise the efficiency of the system.     

基于改进遗传算法的交叉口模糊控制研究*

为了改善城市道路交叉口交通信号控制,降低交叉口车辆延误,提出了一种基于改进遗传算法优化的模糊控制方法。在优化模糊控制器的过程中,为避免出现“早熟”现象,采用改进的自适应遗传算法,在进化过程中动态调整种群中适应度值最大个体的交叉概率和变异概率,使种群进化不会处于一种近似停滞不前的状态。为了检验该控制方法的性能,以交叉口车辆平均延误作为性能评价指标,在相同交通条件下进行了仿真实验。结果表明,相对于普通模糊控制器,经过改进遗传算法优化的模糊控制器能有效减小交叉口车辆的平均延误,提高交叉口的通行能力。     

Predictive car-following scheme for improving traffic flows on urban road networks

Driving behavior is one of the main reasons that causes bottleneck on the freeway or restricts the capacity of signalized intersections. This paper proposes a car-following scheme in a model predictive control (MPC) framework to improve the traffic flow behavior, particularly in stopping and speeding up of individual vehicles in dense urban traffic under a connected vehicle (CV) environment. Using information received through vehicle-to-vehicle (V2V) communication, the scheme predicts the future states of the preceding vehicle and computes the control input by solving a constrained optimization problem considering a finite future horizon. The objective function is to minimize the weighted costs due to speed deviation, control input, and unsafe gaps. The scheme shares the planned driving information with the following vehicles so that they can make better cooperative driving decision. The proposed car-following scheme is simulated in a typical driving scenario with multiple vehicles in dense traffic that has to stop at red signals in multiple intersections. The speeding up or queue clearing and stopping characteristics of the traffic using the proposed scheme is compared with the existing car-following scheme through numerical simulation.     

Automatic intersection and traffic rule detection by mining motor-vehicle GPS trajectories

The generation of road networks from ubiquitous motor-vehicle GPS trajectories has recently gained wide interest. However, few attempts have been made to automatically extract road network properties such as intersections and traffic rules to facilitate the production of high-quality routable maps. For urban street networks, the vehicle trajectory logged by a GPS receiver tends to be straight on streets and curved at intersections although the local deviation exists due to vehicle paths deviating from road centrelines and GPS positioning errors. This paper uses large curved trajectories at traffic intersections and presents novel algorithms for automatically detecting road intersections and traffic rules. Two inherent issues related to GPS trajectories have been resolved using the proposed approach. First, the serious fluctuations of vehicle trajectories due to multipath reflectivity from high-rise buildings have been eliminated, thereby enabling the effective detection of real curved trajectories occurring at traffic intersections. Second, the heterogeneity of traffic density has been considered when using the curved trajectories to automatically detect road intersections. The proposed algorithm was implemented using open-source software libraries and tested using large taxi trajectories collected in Suzhou City, China. A total of 285 at-grade intersections were detected automatically, and dynamic traffic rules were elucidated for each intersection. Compared with the manually interpreted results, the detection results were high quality and provided detailed information for the construction of a routable map.     

城市交通信号局部博弈交互下的学习协调控制

针对传统分布式自适应交通信号控制协调效率受限,并且存在维数灾难问题,建立了城市区域交通信号控制系统模型,将其优化问题建模为局部交叉口交通信号博弈协调控制,提出基于交叉口交通信号控制agent局部信息博弈交互的学习算法。在学习过程中交叉口交通信号控制agent进行局部信息博弈交互,自主调整交通信号控制策略使其逐步学习到最优策略。通过设计不同的交通需求情景,对路网平均延误和平均停车次数进行加权构建性能评价指标,相对于遗传算法和感应控制方法,博弈学习取得更好的交通信号控制效果,其能收敛到最优性能评价指标,其具有更好的交通需求管控能力。     

基于流相关性的网络流量分类

网络流量分类技术对网络安全管理起着非常重要的作用。随着网络和信息技术的发展,传统的基于端口号和深度包检测分类方法的局限性愈发明显,不能对现有的流量进行准确分类。提出一种基于流相关性的半监督网络流量分类算法,并使用MDL-CON高斯混合模型作为聚类模型,通过聚类过程中利用流之间的相关性提高模型的准确度。采用MDL准则解决了高斯混合模型需要人为预先设定类簇数目和高度依赖于初始值的问题。实验结果表明,利用该方法来处理流量分类问题可取得理想的分类效果。     

车载自组织网络中并发广播冲突研究

车载自组织网络(VANET)是移动自组织网络(MANET)在车辆通信领域的特殊应用.其通过专用短程通信(DSRC)技术为车-车(V2V)、车-路(V2I)基单元提供安全可靠的通信服务.然而,VANET网络内广泛存在的并发广播冲突问题使得车辆频繁丢失数据包,遭受潜在的交通安全隐患.针对并发广播冲突问题,目前已有较多的解决...     

平面交叉口交通流动态特征的神经网络模型

平面交叉口的交通控制是交通控制系统理论、方法与实践的重要组成部分，描述平面交叉口实时动态交通需求的模型是实时自适应信号控制的基础依据，本文首先对现有交叉口车流动态特征的研究方法进行描述，在分析车流动态特征产生机理的基础上，指出现有方法的不足，并提出神经网络模型为刻划交叉口动态特征。     

Deep learning and case-based reasoning for predictive and adaptive traffic emergency management

An efficient traffic signal control system (TSCS) should not only be reactive to the current traffic but also be predictive by anticipating future traffic disturbances. In this study, we investigate the potential of using convolution neural network (CNN) in detecting emergency cases and forecasting events that can interrupt the traffic flow. Case-based reasoning (CBR) is then exploited to react to detected and forecasted events. We further develop an adapted Reinforcement Leaning (RL) algorithm in building and enhancing the case bases. The proposed system inherits the advantages of CNN, CBR, and RL, which allow detection, prediction, control, evaluation, and learning in a unified framework. To assess the proposed TSCS, we compare our approach with a set of state-of-art algorithms (e.g., multi-agent preemptive case-based reasoning algorithm and multi-agent preemptive longest queue first—maximal weight matching). The proposed TSCS outperforms the benchmarking algorithms through experiments in various traffic scenarios.

     

基于模糊神经网络的交通干线分层递阶控制

针对城市交通干线协调控制的要求,提出了利用模糊神经网络分层递阶控制的方法。采用两层结构,第一层为控制层。针对单个路口,对下一时间段内路口各个方向的车流量进行预测。并在此基础上计算出下一时间段内各个路口的周期、相序、各个方向上的绿信比;第二层是协调层,综合主干方向的车流状况及各个路口的情况,采用模糊神经网络对各个路口的周期、相位及主干方向的绿信比进行调整。仿真结果表明,该方法优于定时控制,达到了减少车辆的停车次数和延误时间的目的。     

Advances in vehicular ad-hoc networks (VANETs): Challenges and road-map for future development

Recent advances in wireless communication technologies and auto-mobile industry have triggered a significant research interest in the field of vehicular ad-hoc networks (VANETs) over the past few years. A vehicular network consists of vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) communications supported by wireless access technologies such as IEEE 802.11p. This innovation in wireless communication has been envisaged to improve road safety and motor traffic efficiency in near future through the development of intelligent transportation system (ITS). Hence, governments, auto-mobile industries and academia are heavily partnering through several ongoing research projects to establish standards for VANETs. The typical set of VANET application areas, such as vehicle collision warning and traffic information dissemination have made VANET an interesting field of mobile wireless communication. This paper provides an overview on current research state, challenges, potentials of VANETs as well as the ways forward to achieving the long awaited ITS.     

基于云网格集成调度的防拥堵车辆路径规划算法

在道路交通路网中,车辆拥堵问题是流量与路网结构之间相互作用的一个复杂动态过程,通过车辆路径规划,实现对路网网格集成调度,从而提高路网通行吞吐量。传统方法采用并行微观交通动态负载平衡预测算法实现车辆拥堵调度和车辆路径规划,不能准确判断路面上的车辆密度,路径规划效益不好。提出一种基于云网格集成调度的防拥堵车辆路径规划算法,即构建基于Small-World模型的云网格路网模型,采用RFID标签信息进行路况信息采集,实现交通网络拥堵评估信息特征的提取,采用固有模态函数加权平均求得各车道的车辆拥塞状态函数,对所有车道内车辆密度取统计平均可获得簇内的车辆密度。设计交通路网拥堵检测算法来对当前个体道路信息进行一维邻域搜索,从而实现车辆路径规划控制目标函数最佳寻优。通过动态博弈的方式求得车辆防拥堵路径的近似最优轨迹,实现路径规划算法的改进。仿真结果表明,该算法能准确规划车辆路径,实现最优路径控制,从而提高严重拥堵路段的车流速度和路网吞吐性能,性能优越。     

QoS in InfiniBand subnetworks

The InfiniBand architecture (IBA) has been proposed as an industry standard both for communication between processing nodes and I/O devices and for interprocessor communication. It replaces the traditional bus-based interconnect with a switch-based network for connecting processing nodes and I/O devices. It is being developed by the InfiniBand/sup SM/ Trade Association (IBTA) in the aim to provide the levels of reliability, availability, performance, scalability, and quality of service (QoS) required by present and future server systems. For this purpose, IBA provides a series of mechanisms that are able to guarantee QoS to the applications. In previous papers, we have proposed a strategy to compute the InfiniBand arbitration tables. In one of these, we presented and evaluated our proposal to treat traffic with bandwidth requirements. In another, we evaluated our strategy to compute the InfiniBand arbitration tables for traffic with delay requirements, which is a more complex task. In this paper, we evaluate both these proposals together. Furthermore, we also adapt these proposals in order to treat VBR traffic without QoS guarantees, but achieving very good results. Performance results show that, with a correct treatment of each traffic class in the arbitration of the output port, all traffic classes reach their QoS requirements.     

交叉口场景下基于区块链技术的匿名车辆身份认证方案

随着智能交通系统的不断发展,未来车辆在进出交叉口时,将不再受传统信号灯控制,而是通过与交叉口附近的区域服务器进行信息交互,接受区域服务器的统一调度。保证车辆在交叉口通信安全的基础是网络节点的身份认证。基于此,提出一种基于区块链技术的匿名车辆认证方案,在认证中采用基于身份和哈希消息认证码的混合密码认证机制,并将认证结果记录在区块链中。通过仿真测试和结果分析表明,该方案具有匿名性、保护隐私等特性。与其他方案相比,该方案有效降低了通信开销。     

The threat of intelligent collisions

With the advent of intelligent transportation systems (ITS), safer and more efficient roadways are on the horizon. Future generations of in-vehicle ITS will network with nearby vehicles for greater safety and efficiency. Through wireless communication and advanced sensor technologies, intelligent vehicles will learn of nearby vehicles' intentions and dynamics, and of the presence of roadway hazards. These IT technologies will allow safe, tightly spaced vehicle clusters or platoons and coordinate safe lane merges and collision avoidance in intersections and under limited-visibility conditions. For efficiency and economy, the wireless communication will probably take place directly between vehicles. The resulting intervehicle communication (IVC) network will be of the general class of mobile, ad hoc networks without a fixed infrastructure. In contrast to railroad dispatch system or air traffic control systems, which make decisions in a central location and then transmit them to trains or planes, the network will rely on ordinary nodes for routing and network management, spreading the intelligence and infrastructure throughout the network in the vehicles themselves. This article explores the IVC network's potential vulnerabilities and emerging research aimed at mitigating them. Our project, a security architecture under development called SecCar, offers a promising approach to dealing with these risks.     

基于机器视觉的交叉口特种车辆快速通行技术研究

交叉口是道路网络中重要的交通节点,容易产生交通堵塞问题,为了在保证通行安全的情况下提高特种车辆的通行效率,研究基于机器视觉的交叉口特种车辆快速通行技术。优化通行基础采用图像采集及预处理、检测识别和通行控制作为技术框架结构,利用机器视觉技术采集交叉口实时交通图像,通过图像滤波、图像增强等步骤,实现初始图像的预处理。利用Car-YOLO网络识别交叉口通行能力,规划快速通行路线,考虑前车行驶状态,求解特种车辆通行速度,针对车辆所占车道,通过绿灯早启、绿灯周期时间延长等方式控制交叉口信号灯,实现交叉口特种车辆快速通行。实验结果表明:在拥堵和正常通行场景下,优化设计技术的特种车辆通过时间的平均值分别为18.2s、10.1s,事故发生概率分别低于2%、1.4%,具有较好的应用效果。     

车联网D2D通信中最大化频谱资源利用率分配算法

无线通信技术快速发展,终端设备不断增多,为缓解这一现象,提升系统网络容量,针对车联网蜂窝D2D(device to device)通信资源分配问题,提出了一种最大化频谱资源利用率分配算法.该算法以最大化频谱资源利用率为优化目标,在满足车联网通信的基本服务质量(quality of service,QoS)下,通过V2V(vehicle to vehi-cle)和V2P(vehicle to people)共享信道资源来提高频谱资源利用率.首先利用信道状态信息定义的链路增益因子为终端用户找到潜在的通信链路集合;然后证明终端用户复用链路资源时功率分配问题为一个凸优化问题,利用凸优化理论求得最优传输功率;随后求解最优的信道匹配问题,此问题为多对一的加权匹配问题,为降低算法复杂度用KM(Kuhn Munkres)算法来求解.仿真结果表明,所提算法较其他算法能够有效地提升系统吞吐量、提高频谱资源利用率、提升网络性能,优化车联网通信资源分配问题.     

自相似网络业务流建模方法与比较

随着网络服务的发展．多媒体数据逐渐成为网络业务流的主要内容。基于多媒体数据的网络业务流对服务质量(QoS)提出了更高的要求。通常对服务质量的要求必然转化为对网络资源的需求，然而这个需求的量化必须基于一个能够准确描述网络变化的网络业务流数学模型。同时，网络业务流的不同特征以及研究目的的不同使得自相似网络业务流建模成为一个挑战性的问题。近来，一系列的方法被用于自相似网络业务流建模，本文的作者将这些方法分为两类共四种方法。本文全面分析比较了这些方法，并提出一种分类机制用于根据不同的研究需要确定相应的建模方法。