面向车载网的基于AODV优化路由协议的研究

车载网VANETs(Vehicular ad hoc networks)属于新型的通信模型,其可实现车间通信和车与基础设施通信。由于VANETs节点密度动态变化,节点的快速移动、移动模型的局限性等特点,为VANETs的数据传输提出挑战。鉴于此,提出优化的按需距离矢量AODV(Ad Hoc On demand Distance Vector)方案IAODV(Improved AODV)。在城市环境中,AODV在端到端传输时延、数据包丢失率方向性能较差。为此,IAODV采用限定源节点的路由跳数,降低AODV的端到端传输时延,同时为源节点提供备份路由,从而改善数据包丢失率。仿真结果表明,与AODV协议相比,IAODV在端到端传输时延、数据包丢失率得到有效提升。     

基于车联网的合谋攻击研究

车联网系统中的Sybil攻击检测方案大多使用匿名认证来保障车辆隐私安全,但这种认证方式在面对恶意车辆之间合谋发动Sybil攻击时将不具有有效性.本文阐述了现有的车联网身份认证技术中存在合谋攻击,并提出了一种较为有效的检测方案.     

车辆自组织网络中新型NP-CSMA随机多址协议研究分析

智能交通的出现使得车辆自组织网络受到越来越多的关注。车辆自组织网络的动态拓扑结构变化非常剧烈,这对网络的吞吐率、传输速率等性能提出了很高的要求。提出1种具有握手机制协议的自适应多通道双时钟NP-CSMA随机多址接入协议。该协议首先区分2种P-CSMA协议,握手机制成功解决了隐藏的终端问题,双时钟机制减少了平均空闲时间,多通道机制增加通道数量和划分用户优先级的同时提高了系统吞吐率,自适应机制能够使系统在高负载下保持稳定吞吐率。还对该协议的传输速率进行了分析,得出该协议的传输速率相对较高的结论。通过平均周期方法推导吞吐率和传输速率的计算公式,仿真结果与理论推导一致。     

车联网中路边设施的分布式调度策略的研究

为了降低碳排放量和部署成本,利用太阳能给车联网路边设施(Road Side Unit, RSU)供电是一个可行的方法.本文针对太阳能供电的RSU,提出了两个分布式的在线调度策略,旨在最大化服务车辆数.在基于Markov链的调度策略中,采用Markov链表述RSU能量状态,并通过对动作的奖励最大化服务的车辆数;在基于阈值的调度策略中,RSU计算服务车辆时所消耗的能量,并结合自己的能量状态,选择服务的车辆.仿真结果表明,本文提出的在线调度策略增加了服务车辆数.     

Dissemination of safety messages in IEEE 802.11p/WAVE vehicular network: Analytical study and protocol enhancements

Multi-channel IEEE WAVE 1609.4 protocol has been proposed to guarantee the co-existence of safety and non-safety applications over the same Vehicular Ad hoc NETwork (VANET) scenario. While the usage of multi-channel avoids the risk of collisions between applications allocated on different frequencies, its implementation on a single-radio transceiver poses some major concerns about the effective utilization of the channel resources. In this paper, we study the performance of safety applications over multi-channel single-radio VANETs, and we present three novel contributions in this regard. First, we propose an analytical analysis and a simulation study of IEEE 1609.4. We show the harmful impact of synchronous channel switching on the message delay and delivery ratio. Second, we investigate the problem of dissemination of safety broadcast messages over multi-channel VANETs, where the network is intermittently disconnected, due to the alternation of control and service intervals. Finally, we propose a WAVE-enhanced Safety message Delivery (WSD) scheme to enable fast dissemination of safety messages over multi-channel VANETs, while guaranteeing compatibility with the existing WAVE stack. To this aim, we formulate the dissemination problem as a multi-channel scheduling problem. We further introduce cooperation among vehicles to reduce the dissemination latency. Simulation study shows the ability of the WSD scheme to enhance the performance of IEEE 1609.4 in terms of message delay and delivery ratio under different topologies and various applications.     

A novel Intrusion Detection System for Vehicular Ad Hoc Networks (VANETs) based on differences of traffic flow and position

Vehicle ad hoc networks (VANETs) have attracted great interests from both industry and academia, but a number of issues, particularly security, have not been readily addressed. Intrusion Detection System (IDS) as one of the most important approaches to protect network security has been studied adequately in previous literatures. However, the performance of IDSs still needs to be improved to adapt the scenario of VANETs which are very fast moving and highly dynamic. In this paper, we propose a novel IDS that is able to be appropriately used in the wireless and dynamic networks, like VANETs. It mainly contains a novel feature extraction algorithm and a classifier based on an improved growing hierarchical self-organizing map (I-GHSOM) for IDS in VANETs. The proposed feature extraction algorithm is used to quickly extract distinct features from vehicle messages for IDS’s training and test. In the proposed algorithm, two key features including the differences of traffic flow and of position are extracted. The former feature is calculated according to the range of the distance between vehicles, while both a voting filter mechanism and a semi-cooperative mechanism are designed to get the latter feature. Furthermore, in the I-GHSOM-based classifier, for quickly attaining precise classification results, two novel mechanisms (relabeling and recalculating mechanisms) are proposed to relabel the units of GHSOM and check whether the balance of GHSOM structure is broken or not. Simulation results show that the proposed IDS is better than others in the measurement of accuracy, stability, processing efficiency and message scales.     

Routing using reinforcement learning in vehicular ad hoc networks

In vehicular ad hoc networks (VANETs), the frequent change in vehicle mobility creates dynamic changes in communication link and topology of the network. Hence, the key challenge is to address and resolve longer transmission delays and reduced transmission stability. During the establishment of routing path, the focus of entire research is on traffic detection and road selection with high traffic density for increased packet transmission. This reduces the transmission delays and avoids carry-and-forward scenarios; however, these techniques fail in obtaining accurate traffic density in real-time scenario due to rapid change in traffic density. Thus, it is necessary to create a model that efficiently monitors the traffic density and assist VANETs in route selection in an automated way with increased accuracy. In this article, a novel machine learning architecture using deep reinforcement learning (DRL) model is proposed to monitor and estimate the data essential for the routing protocol. In this model, the roadside unit maintains the traffic information on roads using DRL. The DRL predicts the movement of the vehicle and makes a suitable routing path for transmitting the packets with improved transmission capacity. It further uses predicted transmission delays and the destination location to choose the forwarding directions between two road safety units (RSUs). The application of DRL over VANETs yields increased network performance, which provides on-demand routing information. The simulation results show that the DRL-based routing is effective in routing the data packets between the source and destination vehicles than other existing method.     

一种可变尺度的VANETs无结构数据聚合算法

在VANETs的应用中,为了满足应用目的节点对距其远近不同的节点对数据精度的不同要求,聚合算法需要具有能够变尺度调节的能力,形式化地定义了VANETs单节点上的可变尺度数据聚合问题并证明了该问题是NP-难的,同时提出了基于贪心思想的单节点变尺度数据聚合近似算法ESAS.模拟实验表明,在保证可变尺度数据质量前提下,ESA...     

Fuzzy‐based trust prediction for effective coordination in vehicular ad hoc networks

In a vehicular ad hoc network, trust between vehicles is vital to efficiently transmit the data among vehicles. Secure node selection is based on the trust value that a node can calculate during its operations in a dynamic network. This will increase the confidentiality level of the network, thus improving the network performance. However, defining confidentiality is not a crisp task, rather it can range between certain limits. A fuzzy‐based inference engine can be used to optimally define these limits. In this paper, a fuzzy‐based trust prediction model is proposed to effectively compute the trust of other vehicles for the secure path formation in vehicular ad hoc networks. In the designed approach, each vehicle models the trustworthiness of the neighbors in its cognitive range to dynamically select the relay nodes that can be used for data transmission and the election of the appropriate path for routing in vehicular ad hoc network environment. The results and analysis of the proposed model over the standard protocols are presented using simulations. Copyright © 2016 John Wiley & Sons, Ltd.     

LVAP: Lightweight V2I authentication protocol using group communication in VANETs

The authentication protocol is vital for the security of the wireless sensor network to resist the known threats, such as eavesdropping, replay attack, man‐in‐the‐middle attack, etc. In this paper, a lightweight authentication protocol for vehicular ad hoc networks is proposed using the symmetric encryption, the group communication method, and the proactive authentication technique, which not only achieves the desired security goals but also guarantees the practical anonymity and the accountability. The analysis demonstrates that the proposed protocol works properly in the high‐density and the low‐density traffic environment.