移动自组网拓扑发现策略的分析研究*

移动自组网是由移动节点自组织形成的无线网络,由于节点移动、无线通信等特点,构造它的拓扑结构比较困难。通过分析传统的拓扑发现策略,研究了移动自组网拓扑发现策略的最新进展,主要分成蚂蚁算法类和聚簇算法类两种类型,对每个类型中各种算法进行了分类论述,分析了优缺点并进行了综合比较,并讨论了未来需要解决的问题。     

大规模网络上基于图嵌入的可扩展路由方法

大规模网络上理想的路由方法必须同时具有较小的路由表和较短的路径.传统的最短路径路由算法只考虑优化路径,但是路由表需要维护到所有节点的路由信息,故路由表项数至少随网络规模线性增长,因此呈现较差的扩展性.针对基于图嵌入的可扩展路由进行了研究,提出将网络嵌入到由它的支撑图(spanner)导出的度量空间.利用真实网络普遍存在的小世界和无标度拓扑特征,提出了一种嵌入和路由方法--GEROUTE,它用源于高度节点的树形支撑图来构造嵌入,对节点分配较短的标记,使得节点在支撑图上的距离能够由标记推算出来,在节点标记定义的度量空间中使用贪心路由,而节点的路由表只需要存放邻居的标记.分析和仿真表明该路由方法在像Internet的这类图上能够取得比较理想的路由性能,与其他同类方法相比表现更好.     

无线移动网络中k连通m控制集的一个维护算法

构建一个连通控制集（CDS）或者k-连通m-控制集（kmCDS）作为虚拟骨干网来减小路由开销是当前无线网络拓扑控制领域中的一个重要课题。但是,大多数kmCDS构造算法在网络节点移动时都要重新构造kmCDS。文中给出了一个在无线网络中节点移动情况下的kmCDS维护算法。在使用现有算法构造出kmCDS骨干网的无线网络中,本维护算法将节点的移动情形分为三类,并针对各种移动情形分别提出了局部维护策略。理论分析表明在各种移动情况下,该算法都能很好地维持虚拟骨干网kmCDS的性质。     

基于关联规则的贝叶斯网络分类器

关联规则分类器(CBA)利用关联规则来构造分类算法,但其没有考虑分类问题中的不确定性.提出一种基于关联规则的贝叶斯网络分类算法.该算法利用关联规则挖掘算法提取初始的候选网络边集,通过贪心算法学习网络结构,得到比经典的贝叶斯网络分类器TAN更好的拓扑结构.通过在15个UCI数据集上的实验结果表明,该算法取得了比TAN,CBA更好的分类性能.     

熔丝沉积制造中稳固低耗支撑结构生成

熔丝沉积制造(Fused deposition modeling, FDM)是利用熔融塑料丝的一种3D打印技术,热塑料由喷嘴喷出逐层堆积完成打印.由于熔丝只能沉积在已存在物体的上层,因此需要构造支撑结构以支撑悬空部分.针对现有支撑结构生成算法中存在的或结构不稳固或耗材多的缺陷,提出一种以熔丝为支撑单位的树形稀疏支撑结构.与传统算法计算模型表面支撑区域不同,本算法计算每段熔丝需要支撑的区域,使支撑结构更契合熔丝沉积特点.算法还将支撑结构分为三类,将多约束优化问题分解,降低算法复杂度.实验结果表明,本文算法生成的支撑结构算法耗材少、支撑稳定.     

最小方差支撑向量回归

基于支撑向量回归(SVR)可以通过构建支撑向量机分类问题实现的基本思想,推广最小类方差支撑向量机(MCVSVMs)于回归估计,提出了最小方差支撑向量回归(MVSVR)算法.该方法继承了MCVSVMs鲁棒性和泛化能力强的优点,分析了MVSVR和标准SVR之间的关系,讨论了在散度矩阵奇异情况下该方法的求解问题,同时也讨论了MVSVR的非线性情况.实验表明,该方法是可行的,且表现出了更强的泛化能力.     

非结构化环境中基于拓扑约束的地面无人驾驶路径规划算法

针对非结构化环境地面无人驾驶路径规划过程中路径避障以及多车路径冲突的难题,通过同调以及de Rham上同调对环境中障碍物拓扑信息的精确描述,提出一种拓扑约束下基于A*算法且用时更短的路径规划算法.该算法可实现非结构化环境中多无人车全局路径的拓扑分类,从而为多车的协同规划提供一种新的研究思路.此外,结合C-空间动态广义Voronoi图(GVD)的路径拓扑分离特性,提出一种拓扑约束下可用于多无人车全局路径规划的高效算法-----C-空间-GVD-${h_S     

无线认知网络中拓扑控制方法研究

作为下一代通信网络,无线认知网络已成为当前的研究热点;由于节点的移动性,无线网络拓扑结构动态变化,拓扑控制一直是无线网络的难点问题;通过借鉴移动自组织网络(MANETS)中的拓扑控制方法,提出了无线认知网络中基于博弈论和认知功能相结合的拓扑控制方法;无线认知节点能够通过主动决策调节自身节点位置,在保证网络连通性的基础上实现网络覆盖面积最大;仿真实验结果验证了方法的有效性和收敛性.     

无线传感器网络分簇算法研究

无线传感器网络是当前研究和应用的热点,拓扑控制是其研究的重要领域。在与其他传统无线网络拓扑控制机制比较的基础上,归纳了无线传感器网络分簇算法应具有的特性,并对近年来针对无线传感器网络提出的有代表性的分簇算法进行了研究,总结了各种分簇算法的特点和适合的应用场合,重点分析了它们的不足之处,最后指出了无线传感器网络分簇算法未来发展的趋势。     

融合拓扑特征和领域特征的非精确图匹配算法

针对结构模式识别领域中现有图匹配算法对反映图本身拓扑结构的节点特征挖掘不够充分的问题,提出融合拓扑特征和领域特征的非精确图匹配算法。利用建筑学与城市规划学科中的空间句法理论构造图拓扑特征的量化描述,并将其与节点属性和边属性等其他领域的非拓扑特征相结合,构造描述图特征的特征向量,以此为桥梁将结构模式识别问题转化为统计模式识别问题,进而借助支持向量机实现非精确图匹配。不同于其他的图匹配算法,该算法对图的拓扑表达能力强,并且可融合图的领域方面的非拓扑特征,通用性较好。实验结果表明,提出的图匹配算法在不同的图数据集上均具有较高的分类识别率。     

无线传感器网络拓扑控制算法研究进展

对于无线传感器网络而言,拓扑控制是一个基本问题,对网络性能的影响很大,其目标是用最小的能量维持网络拓扑。文章较为详细地介绍了现有的无线传感器网络拓扑控制算法,并分析了各种控制算法的优缺点,探讨了拓扑控制算法今后的重点研究方向。     

Genetic algorithms with immigrants schemes for dynamic multicast problems in mobile ad hoc networks

In this paper, the problem of dynamic quality-of-service (QoS) multicast routing in mobile ad hoc networks is investigated. Lots of interesting works have been done on multicast since it is proved to be a NP-hard problem. However, most of them consider the static network scenarios only and the multicast tree cannot adapt to the topological changes. With the advancement in communication technologies, more and more wireless mobile networks appear, e.g., mobile ad hoc networks (MANETs). In a MANET, the network topology keeps changing due to its inherent characteristics such as the node mobility and energy conservation. Therefore, an effective multicast algorithm should track the topological changes and adapt the best multicast tree to the changes accordingly. In this paper, we propose to use genetic algorithms with immigrants schemes to solve the dynamic QoS multicast problem in MANETs. MANETs are considered as target systems because they represent a new generation of wireless networks. In the construction of the dynamic network environments, two models are proposed and investigated. One is named as the general dynamics model in which the topologies are changed due to that the nodes are scheduled to sleep or wake up. The other is named as the worst dynamics model, in which the topologies are altered because some links on the current best multicast tree are removed. Extensive experiments are conducted based on both of the dynamic network models. The experimental results show that these immigrants based genetic algorithms can quickly adapt to the environmental changes (i.e., the network topology changes) and produce high quality solutions following each change.     

自适应波束天线自组网拓扑控制算法

定向天线能显著提高无线系统的性能和容量,但采用定向天线的自组网拓扑构建问题比全向天线网络复杂。拓扑控制是一种保证网络连通和性能优化的有效手段。基于自适应波束定向天线模型提出一种拓扑控制算法,确定天线的主波束朝向,调整节点的发射功率构建拓扑。算法在保证网络连通的基础上,利用主波束的高增益,降低了节点的发射功率,从而降低节点能耗;同时利用定向天线方向性强的特点,减少了节点间干扰,提高了网络吞吐量。仿真结果表明,算法显著提高了网络性能。     

基于自适应波束天线的局部优化拓扑控制算法

拓扑控制有助于提高adhoc网络的性能,采用定向天线的自组网拓扑控制比全向天线网络更为复杂。基于自适应波束定向天线模型提出一种局部区域优化的拓扑控制算法。该算法利用分簇的思想将网络划分为可重叠的多个区域,区域内节点采用最小生成树(MST)的思想确定邻居关系,通过调整节点发射功率,改变天线波束的朝向、宽度和增益来构建拓扑。算法减小了节点的平均度数,降低了节点的发射功率,从而降低节点能耗,减少了节点间干扰,提高了网络吞吐量,仿真结果表明,算法显著提高了网络性能。     

IMMIGRANTS-ENHANCED MULTI-POPULATION GENETIC ALGORITHMS FOR DYNAMIC SHORTEST PATH ROUTING PROBLEMS IN MOBILE AD HOC NETWORKS

One of the most important characteristics in mobile wireless networks is the topology dynamics, that is, the network topology changes over time as a result of energy conservation or node mobility. Therefore, the shortest path (SP) routing problem turns out to be a dynamic optimization problem in mobile wireless networks. In this article, we propose to use multi-population genetic algorithms (GAs) with an immigrants scheme to solve the dynamic SP routing problem in mobile ad hoc networks, which are the representative of new generation wireless networks. Two types of multi-population GAs are investigated. One is the forking GA in which a parent population continuously searches for a new optimum and a number of child populations try to exploit previously detected promising areas. The other is the shifting-balance GA in which a core population is used to exploit the best solution found and a number of colony populations are responsible for exploring different areas in the solution space. Both multi-population GAs are enhanced by an immigrants scheme to handle the dynamic environments. In the construction of the dynamic network environments, two models are proposed and investigated. One is called the general dynamics model, in which the topologies are changed because the nodes are scheduled to sleep or wake up. The other is called the worst dynamics model, in which the topologies are altered because some links on the current best shortest path are removed. Extensive experiments are conducted based on these two models. The experimental results show that the proposed multi-population GAs with immigrants enhancement can quickly adapt to the environmental changes (i.e., the network topology changes) and produce high-quality solutions after each change.     

Suboptimal network coding subgraph algorithms for 5G minimum-cost multicast networks

To reduce the transmission cost in 5G multicast networks that have separate control and data planes, we focus on the minimum-power-cost network-coding subgraph problem for the coexistence of two multicasts in wireless networks. We propose two suboptimal algorithms as extensions of the Steiner tree multicast. The critical 1-cut path eliminating (C1CPE) algorithm attempts to find the minimum-cost solution for the coexistence of two multicast trees with the same throughput by reusing the links in the topology, and keeps the solution decodable by a coloring process. For the special case in which the two multicast trees share the same source and destinations, we propose the extended selective closest terminal first (E-SCTF) algorithm out of the C1CPE algorithm. Theoretically the complexity of the E-SCTF algorithm is lower than that of the C1CPE algorithm. Simulation results show that both algorithms have superior performance in terms of power cost and that the advantage is more evident in networks with ultra-densification.     

考虑干扰的无线传感器网络拓扑控制问题研究

无线传感器网络的拓扑控制是一个十分重要的技术问题。干扰对传感器网络应用产生了重要的影响,较大的传输干扰将导致信号的碰撞,增大网络延时间。但是,目前的大多数文献没有把干扰作为传感器网络拓扑控制的设计目标和考虑因素之一。本文研究考虑干扰的拓扑控制机制问题,根据传感器网络通信特点,设计了最优的集中式算法和适合合实际应用的次优分布式算法解决该问题。模拟实验结果表明,提出的算法与传统算法相比能有效减少网络干扰、节省能量消耗和减少网络延时,因此是一种新的高效的拓扑控制机制。     

Survivable and delay-guaranteed backbone wireless mesh network design

Backbone wireless mesh networks (BWMNs) consisting of wireless mesh routers are emerging alternatives to implementations of metropolitan area networks (MANs). In a BWMN, gateways connect to the Internet via wireline links and provide Internet access services for users. Due to the limited wireless channel bit rate, multiple gateways are usually required in a BWMN, which costs budget and takes time to set up. In this paper, we study the network topology design and the gateway arrangement so that the construction cost of a BWMN is minimal. Two algorithms, namely, the Predefined Gateway Set Algorithm (PGSA) and the Self-Constituted Gateway Algorithm (SCGA), are proposed for the BWMN design. A genetic algorithm and a proposed enhanced Djikstra's algorithm are employed to search for the low-cost network configuration with constraints such as survivability, link capacity, degree limitation and maximum tolerable delay. Computational results show that the PGSA can give an acceptable network configuration rapidly. In case the gateway cost is high, using the SCGA can lower the network construction cost at the expense of more computational time.     

基于节点增益不同的无线网络拓扑控制技术

无线网络拓扑控制通过调节网络节点的传输功率等措施,从而提升网络性能。无线异构网络因为其网络节点的不同特性,使得适用于无线同构网络的拓扑控制技术无法获得理想的网络性能。以无线网络的图论模型为基础,将信号的能量域指标引入图论模型,从而有效地解决了无线异构网络节点接收增益不同所带来的网络干扰等问题。采用异构无线网络层次类聚等算法,获得接收增益不同条件下的拓扑控制策略。仿真表明,该拓扑控制算法对无线异构网络性能有较大改善。     

一种基于模糊控制的无线传感器网络拓扑控制算法

在无线传感器网络乃至无线网络邻域中,拓扑控制一直是研究热点之一,是无线传感器网络中一种重要的能量节省技术。当前已有很多能量高效的拓扑控制算法,它们试图寻求一个合适的节点发射功率或者一个良好的网络拓扑结构,实际应用中两者往往都需要考虑。提出一种新的拓扑控制方法——HFLTC,该方法基于模糊控制和链路质量评估模型优化进行功率控制,并引入XTC算法思想成链。仿真结果表明,这种把拓扑结构和功率控制结合考虑的方法,更节省网络的平均能耗,提高了整个网络的生命周期。