车载自组织网络中基于停车骨干网络的数据传输

车载自组织网络(vehicular ad hoc networks,简称VANETs)具有网络间歇连通、节点高速移动及动态的网络拓扑结构等特性,如何有效地实现车辆间的数据传输,成为VANETs的重大挑战.现有研究工作基于历史交通流量或历史延迟预测路段当前交通状况的方法并不可靠.此外,要实现高效的数据路由传输,配置大量路边基础设施节点(deploying roadside unit,简称RSU)是一种可行方案,但通常需要额外开销.基于城市区域长时间拥有大量地上停放车辆这一事实,提出了基于停车骨干网络的数据传输策略PBBD(parking backbone based data delivery),不需要配置任何地面基础设施,而是把地面的停放车辆组成一个虚拟的停车覆盖网络,通过该停车覆盖网实现数据的传输.为此,首先,对于每一条道路,把路边和非路边停放车辆组成一个尽可能长的停车簇,并基于这些停车簇组织城市停车骨干网络.其次,设计基于停车覆盖网络的全新数据传输算法来实现车辆间的有效数据传输.基于真实城市地图和交通数据的模拟实验结果表明,与现有的几种数据传输算法相比,PBBD能够以较低的网络传输开销和较小的传输延迟获得较高的数据传输成功率.     

停车边缘计算：车载自组织网络中基于路边停车的任务卸载

为解决车载自组织网络(Vehicle Ad Hoc Neteorks, VANETs)中基础设施建设的不足以及路侧单元(Roadside Uints, RSUs)通信范围受限的问题,提出停车边缘计算的思想,把拥有大量闲置计算资源的路边停放车辆组织成停车簇,令停车簇充当天然边缘计算节点,在RSUs或边缘计算服务器缺失情况下,及时执行周围移动车辆的卸载任务.分析了任务的完成时间,为最大化成功完成的任务数量,设计改进的SAC(Sampling-and-Classification, SAC)算法实现执行任务的停放车辆选择和资源的分配.基于真实城市道路停车调查的模拟实验结果证明,与其他几种任务调度策略相比,本文所提策略具有较高的任务完成率和卸载率.     

车载网中基于模糊逻辑的簇头选择算法

在车载网络(VANETs,vehicular Ad Hoc networks)中,车辆以动态节点方式与其他车辆通信.由于车辆数量的变化和高速移动,通信管理并建立稳定网络成为VANETs最有挑战的项目.因此,簇技术成为解决此挑战的可靠方案之一.利用簇技术将车辆划分不同的群,使得网络更强健.为此,提出基于模糊逻辑的簇头选择算法,记为COHORT算法.在COHORT算法中,车辆利用关于平均速度、邻居密度和链路质量的模糊逻辑评估自己成为簇头的资格,具有最高资格的车辆被选为簇头.仿真结果表明,提出的COHORT算法提高了簇头的生命周期和稳定性.     

停车辅助的车辆边缘计算中的双重任务卸载设计

为解决车辆边缘计算(vehicular edge computing, VEC)中边缘服务器资源受限的问题,提出将路边停放车辆组织成停车簇,辅助实际部署的边缘服务器执行移动车辆产生的计算密集型任务。在此基础上设计双重任务卸载方案,即自上而下的任务分配以及停车簇内部任务分配,保证服务器尽量按时完成卸载的任务,最大限度地提高停放车辆的收益,降低停放车辆执行卸载任务的能耗开销。大量仿真结果表明,该任务调度策略的性能显著优于其它几种对比策略。     

一种基于连接性的VANETs地理机会路由协议

针对车载自组织网络(VANETs)中道路拓扑的限制和车辆的高速移动导致VANETs路由协议面临频繁的连接中断问题而效率低下的问题,综合考虑车辆行驶轨迹和该轨迹上的车流分布信息,动态地选择转发机会最大、预期时延最小的节点作为下一跳,提出了基于道路连接性的地理机会路由协议(CGOP)。仿真结果显示,相比GeOpps和GPSR,提出的CGOP具有更低的平均端到端时延,更高的分组投递率,在连接频繁中断的VANETs中具有良好的鲁棒性。     

基于车辆自组织网络的交通态势检测方法

随着汽车保有量的迅速增加,城市道路交通拥堵变得尤为严重,精确地检测交通态势可以帮助缓解交通问题。为此,提出一种基于车辆自组织网络（vehicular Ad hoc networks,VANETs）的交通态势检测方法---TraSD-VANET（traffic situation detection method based on VANETs）。在该方法中,车辆自动聚簇,然后主动向簇头汇报当前自身的位置和速度信息;簇头根据收到的信息计算簇内的车辆密度和路面上的加权平均速度,之后基于模糊逻辑判断簇内的交通态势。仿真结果表明,在四种车辆场景下,TraSD-VANET检测准确程度比协作检测方法 CoTEC （cooperative traffic congestion detection）平均高16％。该方法在道路交通态势检测中有重要的应用价值。     

基于自适应选路策略的VANETs路由协议

基于车载自组织网络特性,提出一种基于自适应选路策略的VANETs路由协议ASVP。协议针对VANETs的链路频断性,引入携带-转发(carry-forward)机制,使协议能够适用于间断性连接的延迟容忍网络。通过对道路模型分析计算,获得路段连通性度量指标,用于选取最优路径。协议基于道路拓扑将数据包的转发过程分为直路模式和路口模式,并对比分析VANETs中边界模式是否有必要性,路口模式下采用辅助设施进行转发。仿真结果表明,ASVP应用于城市场景中在时延和投递率方面有一定的优势。     

一种城市场景下基于地理位置的跨层路由协议*

针对城市场景下车辆多、移动速度快、道路拓扑结构固定导致VANETs路由协议通信性能很大下降的问题,提出一种适用于城市场景的基于地理位置的跨层多跳路由协议（City Cross-layer Multi-hop Geography Routing,CCMGR）。该协议综合考虑车辆的移动信息和物理层、数据链路层的跨层信息作为数据转发的权值,提出节点选取算法和权重决策方法动态选择权值小的节点进行数据的转发。基于NS-3网络仿真表明,CCMGR路由协议在数据传输成功率和端到端平均延迟性能方面优于GPSR,DSDV,OLSR和DSR协议,适合城市VANETs。     

基于NS-3的VANETs路由协议性能仿真研究

分析了车用自组织网络(VANETs)仿真工具的应用现状,讨论了将NS -3引入VANETs仿真领域的优势.针对VANETs的实际特点,研究了基于NS -3的VANETs仿真平台的构建方法与步骤.依据真实的车辆间通信环境,设计并实现了AODV和OLSR两种路由协议的网络仿真测试方案,并进一步分析了端到端平均时延、分组投递率、吞吐量和时延抖动4项网络性能参数.仿真结果表明,在VANETs应用场景当中,节点运动速度对网络性能的影响较大,而节点密度对网络性能的影响较小;OLSR协议的性能总体要优于AODV协议.     

一种基于分簇的车联网对向协助下载方法

针对目前车联网中提出的协助下载方法的吞吐量低和延迟较长的问题,提出了一种基于动态相邻间距分簇算法的对向协助下载方法.根据道路上车辆的密度不同,动态改变车辆相邻间距使簇保持一个合适的大小;利用分簇算法为车辆分簇;使用分簇的车辆为用户提供协助下载服务.该方法充分利用了道路上行驶的车辆,使更多的车辆加入到协助下载中,提高了用户获取数据吞吐量的同时降低了用户下载数据的延迟.仿真结果表明:使用基于分簇的对向协助下载方法比使用单个车辆对向协助下载时,在盲区的吞吐量提高50％.     

A routing protocol for vehicular ad hoc networks using simulated annealing algorithm and neural networks

Vehicular ad hoc network (VANET) is special type of mobile ad hoc networks which establish communications between adjacent vehicles and also between vehicles and roadside units. Thanks to their dynamic and fast topology changes, inter-vehicular ad hoc networks are like dynamic networks without organizations. Hence, developing a reliable routing algorithm is regarded as a notable challenge in these networks. In this paper, a clustering-based reliable routing algorithm was proposed for VANETs with reliable applications. In this way, simulated annealing was used for appropriate clustering of nodes and the parameters of node degree, coverage and ability were considered in the proposed method. For selecting cluster head, radial basis function neural network was used and a suitable fitness function with velocity and free buffer size parameters was used. Each cluster has two gateway nodes which are used as the communication interface for transmitting data from one cluster to another cluster. The simulation results indicated the efficiency of the proposed method in terms of route discovery rate and packet delivery rate.     

F-Ant: an effective routing protocol for ant colony optimization based on fuzzy logic in vehicular ad hoc networks

Vehicular ad hoc networks (VANETs) are a subset of mobile ad hoc networks that provide communication services between nearby vehicles and also between vehicles and roadside infrastructure. These networks improve road safety and accident prevention and provide entertainment for passengers of vehicles. Due to the characteristics of VANET such as self-organization, dynamic nature and fast-moving vehicles, routing in this network is a considerable challenge. Swarm intelligence algorithms (nature-inspired) such as ant colony optimization (ACO) have been proposed for developing routing protocols in VANETs. In this paper, we propose an enhanced framework for ACO protocol based on fuzzy logic for VANETs. To indicate the effectiveness and performance of our proposed protocol, the network simulator NS-2 is used for simulation. The simulation results demonstrate that our proposed protocol achieves high data packet delivery ratio and low end-to-end delay compared to traditional routing algorithms such as ACO and ad hoc on-demand distance vector (AODV).

     

车载自组网中最远转发机制的可靠性改进

车载自组网是传统的移动自组织网络在交通道路上的应用。在车载自组网中,最远转发机制[1]能够有效降低数据传播跳数,减少冗余发送,但最远转发机制的可靠性将因最远节点失效而受到影响。在分析和实验验证节点高速运动将导致严重的最远节点失效问题的基础上,提出了两种对最远转发机制进行改进的方法:安全距离法和失效预测法。安全距离法选择最接近计算出的安全距离的邻居作为转发节点;失效预测法通过邻居的状态参数对其位置进行预测,进而避免选择那些可能已经脱离通信半径的邻居作为转发节点。多个仿真实验表明,两种改进方法都能不同程度地降低转发节点失效的比例,提高消息传播的可靠性。     

An advanced security scheme based on clustering and key distribution in vehicular ad-hoc networks

Preservation of security is an essential requirement in vehicular ad hoc networks (VANETs) as vehicular communication is vulnerable to attacks. Attackers may exploit VANETs to send bogus information to deceive other vehicles which leads to serious issues. In this paper, we describe an advanced Secure scheme based on Clustering and Key Distribution (SCKD) among members and cluster-heads in VANET. The SCKD is a coordination based algorithm in which nodes are located within different clusters and their cluster heads are chosen from trusty nodes. For a secure end-to-end communication, our scheme deploys the proxy signature, blind proxy signature, hashed message authentication code, and symmetric cryptography. Results show that our scheme preserves security requirements including authentication, confidentiality, data-integrity, non-repudiation, and unforgeability. Since the cost and time computation of key generation and distribution decreases by SCKD compared with other algorithms, our algorithm will be applicable for VANETs.     

ALCA: agent learning–based clustering algorithm in vehicular ad hoc networks

Vehicular ad hoc network (VANET) is an emerging technology which can be used in various applications such as intelligent transport technology, safety applications, etc. But one of the major issues in VANETs is how to cluster the vehicles on the road for efficient operations such as routing, mobility management and generating safety alarms. Clustering of vehicles has been widely used for routing and data dissemination in VANETs. But due to the high mobility of the vehicles/nodes on the road, it is quite difficult to find the exact route in VANETs. Keeping in view of the above issue, in this paper, we propose a new agent learning–based clustering and routing in VANETs. Agents learn from the environment in which they are deployed, and accordingly, their action performed is rewarded or penalized with certain values. Each agent performs its task in collaboration with the other agents, i.e. agents communicate with each other in collaborative manner for information sharing. The deployed agents estimate the mobility of the vehicles, and based upon their learning, clustering of vehicles is performed. An Agent Learning–based Algorithm for Clustering is proposed. The performance of the proposed scheme is evaluated using extensive simulation with respect to the various metrics such as message transmission ratio, percentage of connectivity, node participation, cluster head duration, and connectivity preservation ratio. The results obtained show that the proposed scheme is effective in performing fast clustering and converges quickly to the final solution.     

稀疏交通环境下基于轨迹的车联网数据分发

在稀疏交通环境下,车联网的数据转发机会较少,车辆携带数据时间较长,从而造成较大的数据传输时延.针对该问题,提出了基于车辆轨迹信息的数据转发协议（data dissemination based on trajectory,简称DDBT）.协议采用“携带+连通组件”思想,通过分析双向交通路段延迟特征,建立了端对端传输延迟模型,提出了在路口接入点协助下基于最小传输延迟期望的车辆留存副本多径转发原则.理论分析及仿真实验结果表明,提出的DDBT协议在稀疏交通环境下表现出较好的数据传输延迟性能.     

车联网中beacon消息传输频率研究

车联网(VANETs)提供车与车之间的车间通信(V2V)和车与路旁设施(V2I)间的通信。VANETs中存在两类消息:beacon消息和安全消息。车辆周期地交互车辆的beacon消息,仅在紧急情况下才广播安全消息;而beacon消息的传输频率受多个因素影响。为此,提出基于模糊逻辑的自适应beacon传输频率方案,利用模糊逻辑系统处理多输入决策系统的优势,将信道忙碌因子、车辆移动因子和数据包传递率作为系统输入,通过模糊逻辑系统决策算出最优的beacon传输频率。使用NS-3仿真软件分析VAB方案的平均等待时间,总体传输时延和平均邻居数等参数。仿真实验表明,VAB方案的性能明显优于JSRC和BRAIN-F方案。     

Game theoretic approach for real-time data dissemination and offloading in vehicular ad hoc networks

Due to high velocity of the vehicles, data dissemination and mobile data offloading are most difficult tasks to be performed in vehicular ad hoc networks (VANETs). In recent years, due to an exponential increase in the data generated from various sources such as smart devices, gadgets, and actuators, there arises a need of usage of an efficient communication infrastructure to handle the aforementioned issues. Most of the earlier solutions reported in the literature for data offloading problem have used the cellular communication, which may be congested in handing a large number of requests from community of users. This may result a performance bottleneck in terms of call drops and data dissemination to the other vehicles in the VANET environment. Also, these schemes lack a comprehensive approach of data dissemination to meet the quality of service (QoS) in real time. Hence, to overcome this problem, some of the mobile data can be disseminated using the existing vehicular infrastructure and Wi-Fi access points (APs). In this paper, we propose a new schedule based on game theoretic approach where the APs and vehicles act as players in a game and compete for offloading the cellular data. The proposed scheme is based on the selection of the best vehicle or AP based on the utility of the players (vehicles and APs) in the game. The utility of vehicle and AP is decided based on the parameters such as distance, velocity, connectivity to destination, bandwidth, and area of the network. A novel algorithm has been designed using the proposed game theoretic approach for handling mobile data offloading and data dissemination. The proposed solution not only successfully offloads the data but also maintains QoS with respect to the parameters such as end-to-end delay, message progress, and message dissemination speed. Results obtained confirm the superiority of the proposal in comparison with the other existing schemes. Specifically, the proposed scheme achieves improvement of 4.16 and 20.5 % in message progress, 18.91 and 4.75 % in extra messages generated, 11.26 and 54.94 % in message dissemination speed, and 78.71 and 87.94 % in end-to-end delay in sparse network as compared to GyTAR and GPCR, respectively.