认知无线Ad Hoc网络中的路由尺度和算法设计

在认知无线Ad Hoc网络中,路由问题面临巨大挑战。由于各认知节点可用频谱的时变性、多样性和差异性,传统的路由尺度不再适用于认知无线Ad Hoc网络。本文在自组织认知网络环境下,应用主用户信道的使用模型,同时考虑次用户之间的干扰情况,提出了两类路由尺度和算法,仿真结果表明,这两类路由尺度都能很好地反映端到端性能。     

分布式认知无线电网络多路径路由协议研究综述

为了深入研究分布式认知无线电网络的多路径路由问题,阐述了分布式认知无线电网络基本特征以及多路径路由设计面临的问题与挑战。分布式认知无线电网络多路径路由采用多路径并行传输,可有效降低传输时延、增加网络吞吐量与传输可靠性、实现网络负载均衡。根据路由优化目标不同,从吞吐量、带宽、干扰、时延、负载均衡和路由发现六方面对近年来多路径路由协议的主要研究成果进行了分类,然后逐类对多路径路由协议进行了分析和讨论,最后分别从分布式认知无线电网络基本特征的适应性与多路径路由协议特征两个方面进行了比较,并展望了分布式认知无线电网络多路径路由协议需进一步研究的方向。     

VANETs路由协议的研究进展

车辆自组织网络是传统自组织网络派生出的一个分支,其与应用场景高度相关.传统路由协议不能有效的适用于车辆自组织网络,因此,针对车辆自组织网络提出了许多新的路由协议.首先在总结车辆自组织网络的特性后,分别介绍了单播路由、广播路由和地域性多播路由的概念,然后着重分析和总结了近年来具有代表性的路由协议的核心路由机制及其优缺点,...     

卫星网络路由技术

由于卫星网络路由技术应用环境的特殊性,研究人员不能根据实际的网络学习环境,只有通过模拟,和仿真工具NS2是指网络仿真设备版本2,NS(网络模拟设备)是开放源码软件仿真平台,与它对网络技术发展研究的科研人员,NS2已包含卫星节点,作为卫星链路模块但这些模块相对比较简单,只有单一层卫星网络.因此,本文提出了多个卫星网络在NS2仿真,实现层间通信网络路由协议多个卫星的研究和开发方法.     

认知无线电网络层路由问题研究

在认知无线电中,网络层用于确定如何在网络中找到一条从源节点到目标节点的路径,且数据路径应当是适应频谱状况的,即尽可能绕过主用户频繁出现的区域。由于频谱是随机的,因而数据路径也可能是随机的。文章分析认知无线电网络中路由面临的挑战,介绍认知无线电网络中的两种路由方案:静态路由和动态路由,给出静态路由频谱选择阶段的5个指标,研究静态路由下一跳选择阶段的优化问题,最后提出认知无线电网络动态路由的实现方案。     

基于蚁群算法的网络路由最优路径判断模块设计与实现

网络中节点的能量是有限的,网络拓扑结构具有波动性,导致传统网络路由算法不能有效适应这些变化,自组织性较差,无法及时获取最优路径,大大降低网络性能。因此,设计基于蚁群算法的网络路由最优路径判断模块。其以FPGA为控制核心实现硬件设计,具体包括控制模块、存储器模块、寻求后续节点集模块、采集后续节点模块、状态调整模块、信息素调整模块和最优路径判断模块。模块实现部分给出了蚁群算法的核心代码。实验结果表明,所设计的最优路径判断模块具有较高的收敛速率,获取的路径更短,能够延长网络的运行周期。     

基于闲时逆寻和路由学习机制的优化AODV路由协议

对AODV协议在不同MANET网络环境中的性能缺陷进行了研究,提出了节点闲时反向路由搜索机制和邻居节点路由监听学习机制,并进一步提出了优化的AODV路由协议O-AODV。利用O-AODV协议在MANET网络中产生多个局部路由,从而增加网络中的路由冗余度,提高路由发现的效率,加快故障路由的本地修复。仿真实验结果表明,O-AODV协议提高了分组投递率,降低了端到端延时,有效地减少了网络中的重路由开销,在大规模和拓扑变化快的网络环境中优于AODV协议。     

无线自组织网络中的路由技术研究

路由技术是无线自组织网络组网的基础和关键环节.文章介绍了当前无线自组织网络路由技术研究的现状,在此基础上,详述了无线自组织网络中具有代表性的路由协议,并对这一领域未来的发展趋势作了展望.     

认知Ad-Hoc网络基于信道信息的路由算法

由于认知网络中信道具有动态时变特性,路由选择和信道分配成为认知Ad-Hoc网络亟待解决的问题。为此提出一种基于信道信息的改进路由算法,结合节点的信道共用度和最小条数作为路由度量,通过选取较稳定的节点,增强链路的稳定性。仿真结果表明,对于信道变化波动较大的环境,改进的路由算法具有很好的路径稳定性和链路修复能力。     

具有上下文认知的高能效多径路由算法研究

针对移动Ad Hoc网络终端能量资源受限对全网路由的影响,提出了一种基于上下文认知的高能效多径Ad Hoc网络路由算法.该算法面向Ad Hoc网络环境综合考虑节点的能耗、传输路径的能耗和多径路由选择等要素.经过NS2仿真实验并且与其他相关路由算法进行比较,结果表明在相同的实验环境下,该路由算法能够有效地节约能量,降低了端到端的时延,提高网络的吞吐量.     

Routing in Cognitive Networks

Cognitive networks are capable of learning and reasoning. They can dynamically adapt to varying network conditions in order to optimize end-to-end performance and utilize network resources efficiently. This paper proposes a cognitive network routing scheme that includes a context information collection entity,a route manager entity,a route reconfiguration entity,and reasoning and learning entity.     

Safari: A self-organizing, hierarchical architecture for scalable ad hoc networking

As wireless devices become more pervasive, mobile ad hoc networks are gaining importance, motivating the development of highly scalable ad hoc networking techniques. In this paper, we give an overview of the Safari architecture for highly scalable ad hoc network routing, and we present the design and evaluation of a specific realization of the Safari architecture, which we call Masai. We focus in this work on the scalability of learning and maintaining the routing state necessary for a large ad hoc network. The Safari architecture provides scalable ad hoc network routing, the seamless integration of infrastructure networks when and where they are available, and the support of self-organizing, decentralized network applications. Safari’s architecture is based on (1) a self-organizing network hierarchy that recursively groups participating nodes into an adaptive, locality-based hierarchy of cells; (2) a routing protocol that uses a hybrid of proactive and reactive routing information in the cells and scales to much larger numbers of nodes than previous ad hoc network routing protocols; and (3) a distributed hash table grounded in the network hierarchy, which supports decentralized network services on top of Safari. We evaluate the Masai realization of the Safari architecture through analysis and simulations, under varying network sizes, fraction of mobile nodes, and offered traffic loads. Compared to both the DSR and the L+ routing protocols, our results show that the Masai realization of the Safari architecture is significantly more scalable, with much higher packet delivery ratio and lower overhead.     

认知Ad Hoc网络中的路由综述

认知AdHoc网络是一种特殊的无线网络,与传统的无线AdHoc网络不同,它需要考虑主用户与认知用户的接入优先级问题,因此传统的路由技术不适用于认知AdHoc网络。简单介绍了认知AdHoc网络的架构,列举了传统AdHoc无线网络的路由算法并分析它们的缺点。为了适应认知AdHoc网络的特点,基于不同的参数指标,研究人员提出了一系列高效的算法,将重点分析这些路由算法。     

基于增强学习的协作主动路由信息交换机制

分组交换网是当今全球通信基础设施的重要部分，IP网络终将与其他网络相互融合，演化成为新一代网络技术，现有路由协议的实现是单一的，并且将数据库、最优路径计算和网络状态分发机制捆绑在一起。主动路由选择是一个把上述困难作为目标的新路由选择策略。主动路由选择机制被设计用来提供基于用户服务模式的路由选择。本文提出了一种基于增强学习的协作主动路由信息交换机制，主动路由代理获悉网络基础结构不同属性的当前状态，并在这个基础上建立和维护路由表，实现主动路由选择机制。     

Integrating machine learning in ad hoc routing: A wireless adaptive routing protocol

The nodes in a wireless ad hoc network act as routers in a self‐configuring network without infrastructure. An application running on the nodes in the ad hoc network may require that intermediate nodes act as routers, receiving and forwarding data packets to other nodes to overcome the limitations of noise, router congestion and limited transmission power. In existing routing protocols, the ‘self‐configuring’ aspects of network construction have generally been limited to the construction of routes that minimize the number of intermediate nodes on a route while ignoring the effects that the resulting traffic has on the overall communication capacity of the network. This paper presents a context‐aware routing metric that factors the effects of environmental noise and router congestion into a single time‐based metric, and further presents a new cross‐layer routing protocol, called Warp‐5 (Wireless Adaptive Routing Protocol, Version 5), that uses the new metric to make better routing decisions in heterogeneous network systems. Simulation results for Warp‐5 are presented and compared to the existing, well‐known AODV (Ad hoc On‐Demand Distance Vector) routing protocol and the reinforcement‐learning based routing protocol, Q‐routing. The results show Warp‐5 to be superior to shortest path routing protocols and Q‐routing for preventing router congestion and packet loss due to noise. Copyright © 2011 John Wiley & Sons, Ltd.     

基于业务感知和策略选择的认知路由算法

提出了一种基于业务感知和策略选择的认知路由(cognitive network routing)算法。该算法面向认知网络环境综合考虑了网络资源、业务流、策略选择等要素,在获知网络中业务流的宏观特征和需求的前提下,首先通过离线资源分配将网络资源按照业务流的固有分配特征预先配置给不同类型的业务流,然后通过在线路径计算实时得出各个业务流的最优路径。仿真实验验证了认知路由算法的有效性,对比于MIRA、SWP路由算法,CNR算法可以提高网络资源利用率,避免因资源不均衡产生的网络拥塞,能够在业务流和资源2个层面优化网络性能。     

面向动态拓扑网络的深度强化学习路由技术

针对现有智能路由技术无法适用于动态拓扑的不足,提出了一种面向动态拓扑的深度强化学习智能路由技术,通过使用图神经网络近似PPO(Proximal Policy Optimization)强化学习算法中的策略函数与值函数、策略函数输出所有链路的权值、基于链路权值计算最小成本路径的方法,实现了路由智能体对不同网络拓扑的泛化....     

Multipath Cognitive Routing in Integrated Multicast Satellite-HAP System Structures

A new multilayered inter satellite-high altitude platform(HAP)system routing algorithm is proposed,which is mainly based on multipath routing to ensure the network reliability.The proposed multipath routing scheme principally relies on splitting the traffic between different paths to make the best utilization of multiple routes.Linear programming is the main method used for multipath selection.The major constraints to the quality of service(QoS)(delay and link utilization)have been taken into account to meet the criteria of the advanced multimedia applications.Due to the effect of link utilization,the system encounters traffic flow oscillation between paths over time,which affects the system performance.Hence,to fix this issue,we propose a cognitive routing algorithm which reacts to the long-term changes of the traffic loads rather than short-term ones.The performance of the proposed routing techniques has been evaluated using appropriate simulation models and implemented by Matlab.     

基于并行BP神经网络的路由查找算法

结合Bloom-filter算法和并行反向传播神经网络,提出了一种新的基于并行神经网络的路由查找算法(BFBP)。该算法满足路由查找的需求,只需学习路由条目的网络ID,且易于扩展到IPv6地址查询。研究结果表明,相比于己有的神经网络路由查找方法,该算法需要学习的条目数平均减少了520倍,提高了学习效率,为神经网络应用于路由查找创造了有利条件。     

ArgMax and ArgMin: transitional probabilistic models in cognitive radio mesh networks

The erratic nature of spectrum availability and diversity imposes the use of a probabilistic framework for channel selection in cognitive radio networks protocol design. In this work, two probability distributions called ArgMax and ArgMin are proposed, which have broad applications in channel selection mechanisms, routing, and media access control protocols. The ArgMax probability distribution locates the maximum random variable among a set of random variables, while the ArgMin locates the minimum random variable. We show that the ArgMax probability distribution is a better candidate than the frequently used odds‐on‐mean probability distribution through theoretical analysis and simulation. The ArgMin probability distribution has a variety of applications and is shown to be useful in achieving a lower bound on the network's minimum spectral capacity. In simulation, we develop a probabilistic selection routing procedure (PSRP) that adopts the ArgMax probability distribution to guide packets throughout the network. The stochastic framework of probabilistic selection routing procedure is also an appropriate skeleton for building stochastic‐based routing protocols for dynamic networks such as cognitive radio networks. The simulation results suggest that ArgMax enables the routing scheme to adapt to the network dynamic more quickly and to more accurately locate the best candidate to route to than the odds‐on‐mean probability distribution. The ArgMax enhances the network throughput and end‐to‐end delay by over 30% when network load increases. Copyright © 2013 John Wiley & Sons, Ltd.