An energy-efficient geographic routing protocol design in vehicular ad-hoc network

The combination of vehicles and wireless communication has resulted in a promising future for vehicular ad hoc networks (VANETs). The VANET is a kind of network that can be built randomly, quickly and temporarily without any infrastructure. The routing of data in a VANET is a challenging task due to the high dynamics, unstable connection environments and transfer direction limit (real road planning) involved. However, it has been discussed that radio obstacles, as found in urban areas, have a significant negative impact on the performance of location based routing. Therefore, the design of a suitable routing protocol for VANETs is an important issue, especially in regard to inter-vehicle communication applications. This paper proposes a energy-efficient geographic routing algorithm that uses the direction, density and distance between nodes in the crossroad routing strategy, to improve the link stability. We compare the novel scheme, ad hoc on-demand distance vector, and dynamic source routing (DSR) for packet loss rate and average end-to-end delay in VANETs to reduce the power consumptions. The simulation results are obtained which demonstrate the power effectiveness of our proposed routing strategy.     

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.     

Analytical evaluation of average delay and maximum stable throughput along a typical two-way street for vehicular ad hoc networks in sparse situations

Intermittent connectivity is an intrinsic feature of vehicular ad hoc networks (VANETs) in sparse situations. This type of network is in fact an example of delay and disruption tolerant networks (DTNs). In this paper, we focus on a typical two-way street and analytically evaluate the maximum stable throughput and the average delay for packet forwarding along the street. To this end, we map the mobility patterns of the vehicles with different speeds onto suitable parameters of a BCMP queueing network and derive the location density of vehicles. Then, we employ another queueing network in order to model opportunistic multi-hop packet forwarding along the street with respect to the specifications of MAC and routing schemes. We propose a two-mode MAC scheme suitable for DTNs with predictable mobility patterns. We also consider the effect of vehicles’ velocities and opportunistic relaying for routing schemes. In our analysis, we evaluate the average delay and the maximum stable throughput for the proposed MAC and routing schemes. In the last part of the paper, we show the efficiency of the proposed analytical approach by some numerical results and confirm our analysis by simulation.     

一种安全的Ad Hoc网络路由协议SGSR

Ad Hoc网络作为一种无线移动网络,其安全问题,特别是路由协议的安全备受关注。针对现有适合移动Ad Hoc网络的链路状态路由协议GSR无法防范恶意节点伪造、篡改、DoS攻击的现状,本文提出了一种在移动Ad Hoc网络中抵抗单个节点恶意攻击的安全路由协议SGSR,给出了认证协议的形式化证明,并对路由协议进行仿真和性能分析。     

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.     

COBRA:车载网络中基于协作的大数据传输增强机制

车载网络利用车载无线通信设备在车辆移动过程中组网.研究者们已提出了大量的路由算法用于车载网络中的多跳数据传输,但是这些算法大都假设车辆间传输带宽不受限制,从而其性能会随转发数据量的增加而降低.车载网络中用于大数据量传输的路由增强机制COBRA,利用同向行驶车辆间的拓扑稳定特性延长数据在车辆间的传输时间,利用擦除编码增加传输可靠性.实验证明,当传输数据量较大或带宽受限时,COBRA机制能以较低的开销提升现有典型车载网络路由协议的传输延时与可靠性能.     

A location routing protocol based on smart antennas for ad hoc networks

Ad hoc networks are a type of mobile networks that function without any fixed infrastructure. One of the weaknesses of ad hoc networks is that route between a source and a destination is likely to break during communication. Location-based routing algorithms are promising in reducing routing overhead in ad hoc networks. Existing location-based routing algorithms employ the Global Positioning System (GPS) as their location information provider, and synchronize information network-wide. Nonetheless, these protocols have some limitations because the use of GPS is not realistic in certain circumstances. Since smart antenna is able to infer direction and ranging information between pairs of ad hoc node, the obtained local position information can be used to improve routing mechanism. This work proposes a novel Location-Enhanced On-Demand (LEOD) routing protocol which is based on smart antenna technique. The LEOD protocol utilizes local position instead of global position to discover routes and make routing decision for the ad hoc network. Theoretical computation and simulation results show that data packet loss rate decreased significantly compared to other methods well-documented in the literature. In addition, this protocol reduces the network control overheads and the power consumption. It also improves network average throughput.     

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.     

移动自组网中一种面向群组移动性的位置服务协议

基于位置信息的路由协议被广泛认为是一种可扩展的移动自组网路由解决方案.大多数此类协议假定可通过位置服务协议获取结点的位置信息.研究人员现已提出多种位置服务协议.但是,它们在大规模网络环境中的可扩展性不好,或者在高动态网络环境中可靠性不高.本文提出了一种适用于大规模移动自组网的面向群组移动性的可扩展及可靠的位置服务协议.模拟结果表明此协议比SLURP协议更适合于具有群组移动性的大规模移动自组网.     

A collaborative protocol for anonymous reporting in vehicular ad hoc networks

Vehicular ad hoc networks (VANETs) have emerged to leverage the power of modern communication technologies, applied to both vehicles and infrastructure. Allowing drivers to report traffic accidents and violations through the VANET may lead to substantial improvements in road safety. However, being able to do so anonymously in order to avoid personal and professional repercussions will undoubtedly translate into user acceptance. The main goal of this work is to propose a new collaborative protocol for enforcing anonymity in multi-hop VANETs, closely inspired by the well-known Crowds protocol. In a nutshell, our anonymous-reporting protocol depends on a forwarding probability that determines whether the next forwarding step in message routing is random, for better anonymity, or in accordance with the routing protocol on which our approach builds, for better quality of service (QoS). Different from Crowds, our protocol is specifically conceived for multi-hop lossy wireless networks. Simulations for residential and downtown areas support and quantify the usefulness of our collaborative strategy for better anonymity, when users are willing to pay an eminently reasonable price in QoS.     

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.     

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.     

PassCAR: A passive clustering aided routing protocol for vehicular ad hoc networks

Vehicular ad hoc networks (VANETs) are a promising architecture for vehicle-to-vehicle communications in the transportation field. However, the frequent topology changes in VANETs create many challenges to data delivery because the vehicle velocity varies with time. Thus, designing an efficient routing protocol for stable and reliable communication is essential. Existing studies show that clustering is an elegant approach to efficient routing in a mobile environment. In particular, the passive clustering (PC) mechanism has been validated as a more efficient approach compared to traditional clustering mechanisms. However, the PC mechanism was primarily designed for mobile ad hoc networks (MANETs), and may be unsuitable for constructing a cluster structure in VANETs because it does not account for vehicle behavior and link quality. In this paper, we propose a passive clustering aided routing protocol, named PassCAR, to enhance routing performance in the one-way multi-lane highway scenario. The main goal of PassCAR is to determine suitable participants for constructing a stable and reliable cluster structure during the route discovery phase. Each candidate node self-determines its own priority to compete for a participant using the proposed multi-metric election strategy based on metrics such as node degree, expected transmission count, and link lifetime. Simulation results show that, compared with the original PC mechanism, PassCAR not only increases the successful probability of route discovery, but also selects more suitable nodes to participate in the created cluster structure. This well-constructed cluster structure significantly improves the packet delivery ratio and achieves a higher network throughput due to its preference for reliable, stable, and durable routing paths.     

Automatic tuning of communication protocols for vehicular ad hoc networks using metaheuristics

The emerging field of vehicular ad hoc networks (VANETs) deals with a set of communicating vehicles which are able to spontaneously interconnect without any pre-existing infrastructure. In such kind of networks, it is crucial to make an optimal configuration of the communication protocols previously to the final network deployment. This way, a human designer can obtain an optimal QoS of the network beforehand. The problem we consider in this work lies in configuring the File Transfer protocol Configuration (FTC) with the aim of optimizing the transmission time, the number of lost packets, and the amount of data transferred in realistic VANET scenarios. We face the FTC with five representative state-of-the-art optimization techniques and compare their performance. These algorithms are: Particle Swarm Optimization (PSO), Differential Evolution (DE), Genetic Algorithm (GA), Evolutionary Strategy (ES), and Simulated Annealing (SA). For our tests, two typical environment instances of VANETs for Urban and Highway scenarios have been defined. The experiments using ns- 2 (a well-known realistic VANET simulator) reveal that PSO outperforms all the compared algorithms for both studied VANET instances.     

车载自组织网络路由协议的仿真研究

本文简要介绍了车载自组织网络和移动自组织网络中两个典型的路由协议:表驱动路由协议DSDV和按需路由协议AODV;还介绍了一个实用的移动模型,使仿真实验更加接近现实。在Linux下使用网络仿真工具NS2对这两个典型协议进行仿真,并对仿真结果进行分析比较和性能评价,得出:AODV和DSDV协议都不太适用于车辆自组织网络,所以设计适合车辆网路的协议是急需解决的问题。     

移动自组织网络路由选择算法研究进展

移动Ad Hoc无需基础设施,可支持高动态的移动数据通信,具有广泛应用的前景。然而,其高动态特性导致互联网现有路由协议不再适用。近年来学者针对Ad Hoc网络提出了大量的路由选择算法。给出了各种路由技术分类方法,简述了Ad Hoc网络的基本路由协议和路由选择算法,进而从预测模型、能量模型、位置信息、服务质量控制和安全支持等5个角度,深入分析了当前Ad Hoc网络路由选择算法的最新研究进展。预测模型通过基于历史信息的移动预测,降低了路由选择的时间代价;能耗模型则采用适当的数学模型来描述网络能耗情况,在选路过程中实现了分组传送的能耗最小化和能量负担均衡之间的权衡;基于位置的路由选择算法根据节点的地理位置来标识目的地,从而利用该地理位置信息进行路由选择;服务质量感知的路由选择算法在本地计算中考虑带宽、延迟、能量和电池生命周期,从而在选路过程中提供了服务质量支持;安全路由选择算法则利用适合移动Ad Hoc网络的密钥、哈希链、电子签名等技术对协议交互消息进行加密来保障网络安全。最后从通信开销、计算和存储开销、路径数量、关键节点的存在性以及算法类型等方面详细对比总结了近年来提出的30余种典型路由选择算法,指出了各自的特点...     

An efficient bow-based on-demand QoS routing protocol for MIMO ad hoc networks

The multiple inputs multiple output (MIMO) architecture supports smart antennas and MIMO links is now a popular technique for exploiting the multi-path, spatial multiplexing, and diversity gain to provide high spectral efficiencies and performance improvement in wireless ad hoc networks. In this work, we propose a new multi-path on demand quality-of-service (QoS) routing architecture, looked like a bow and called as bow structure, in MIMO ad hoc networks. A bow-based MIMO ad hoc networks routing protocol, named as BowQR, is also proposed to support QoS requirement and to improve the transmission efficiency. Each bow structure is composed of rate-links and/or range-links on demand to provide multi-path routing and satisfy the bandwidth requirement. Two types of transmission links, the rate-link and range-link, exploit the spatial multiplexing and spatial diversity to provide extremely high spectral efficiencies and increase the transmission range. Finally, the simulation results show that our BowQR protocol achieves the performance improvements in throughput, success rate, and average latency.     

异构无线网络中的Ad Hoc路由及QoS分析

首先分析了纯Ad Hoc网络环境下具有QoS保证的几种典型路由协议,然后阐述了异构无线网络的体系架构以及异构网络环境下的Ad Hoc路由,包括基于节点位置信息的路由分级路由、提高网络容量的多跳中继路由、实现网络负载均衡的路由,以及跨层路由协议。最后,总结了在异构网络环境下提出的基于Ad Hoc网络多跳中继路由的负载均衡策略的研究工作,分析了仿真结果。     

一种基于三角模糊数的VANETs机会路由算法*

分析了现有车用自组织网络（VANETs）的路由算法,提出一种新的基于三角模糊数的机会路由算法。新算法将转发结点距离目标结点的距离、到达目标结点的方向、行驶速度向量、重传次数、车流状况等因素作为目标函数进行分析计算,采用熵权系数法确定各因素权重。路由过程中,贪婪选择向量值最大的节点转发数据包;遇到网络不连续时,将采用“存储－携带－转发”的机会路由策略。仿真结果表明,该算法能够较好的适用于VANETs各种场景。     

高能量节点驱动的按需距离矢量路由协议

无线自组织网络中的按需距离矢量路由协议(AODV)没有考虑到能量消耗的均衡性和网络生命期的问题。针对AODV的这一缺点,提出了一种高能量节点驱动的AODV协议(HN-AODV)。此协议将高能量节点驱动的策略应用于按需路由发现过程,尽量选择能量较高的节点来承担转发任务,以此来平衡网络能耗。仿真结果显示,HN-AODV在基本不降低数据传输性能的前提下,显著提高了网络生命周期。这种高能量节点驱动的方案同样可以运用在其它类似的反应式路由协议中。