基于扩展力学模型的网络拓扑图布局算法*

针对现有网络拓扑图布局算法多以节点分布均匀为目标,没有考虑边的布局,可能会导致生成的拓扑图中边布局不清晰,提出一种基于扩展力学模型的网络拓扑图布局算法。该算法通过引入点边斥力保证边布局清晰,通过节点坐标的分层分配可以方便地满足某些网络的拓扑图层次布局需求。仿真结果表明,扩展力学模型生成的拓扑图节点分布均匀,节点和边之间距离合理,布局效果得到提高。     

Ad Hoc网络中基于方向性天线的分布式拓扑控制算法

提出了一种基于方向性天线的分布式拓扑控制算法,可以同时通过调整网络中各节点的发射功率和改变节点天线的方向来对网络的拓扑进行控制,每个节点逐渐增大它的发射功率直到该节点在其方向性天线的每个扇区内找到足够数量的邻节点为止.在这种基于方向性天线的分布式拓扑控制算法的基础上又使用了两种不同的拓扑平面化优化算法,进一步删除了拓扑图中多余的交织边,使得网络最终的结构为一幅平坦图.由于每个节点使用了较低的发射功率以及算法形成的网络拓扑图中的平均节点度数较小,从而提高了整个网络的使用寿命,减少了节点间的干扰.仿真结果充分说明了算法的有效性.     

一种适用于无线传感器网络的拓扑控制算法

无线传感器网络拓扑控制算法对于延长网络的生存时间、减小通信干扰、提高路由协议和MAC协议的效率等具有重要的意义.在分析XTC(eXemplary Topology Control)算法的基础上,提出一种改进的基于局部网络信息的分布式拓扑控制算法M-XTC(M0dIfied-XTC).改进算法保持了XTC算法简单、实用,不需要节点位置信息,适用于普通节点、异构网络和三维空间等优点,并且更有利于延长网络的生存时间,具有更好的实时性和鲁棒性.     

移动Ad hoc网络中基于拓扑控制的节能算法

如何降低节点能耗,延长节点生存时间是移动Ad hoc网络的一个研究热点,对此提出了一种基于拓扑控制的节能算法ECA/TC（Energy Conservation Algorithm with Topology Control）。该算法在RNG图的基础上,采用邻节点消除机制,有效降低了节点的传输功率及广播消息在网络中的转发次数。仿真结果显示该算法具有较好性能,能够提高网络能效。     

基于势博弈的WSN分布式拓扑控制算法

针对节点能量有限的无线传感器网络（WSN）,设计一种有效延长网络生命时间的网络拓扑控制算法非常有必要。考虑到节点是自私的,每个节点想着如何减少自身能耗提高自身利益,却忽视了网络整体利益。为了解决该冲突,利用势博弈存在纳什均衡的性质,提出了基于势博弈的分布式拓扑控制算法（Potential Game and Distributed Topology Control, PGDTC）,它是种能量高效和能量平衡的拓扑控制算法。仿真结果表明：相比于现有的一些拓扑控制算法,PGDTC算法能够有效的延长网络生命时间。     

基于冗余节点选择模型的水下传感器网络拓扑修复

现有水下传感器网络的拓扑修复算法大多只是完成网络连通性修复,未考虑节点能耗过快造成网络寿命缩短的问题。为此,提出一种基于冗余节点选择模型的拓扑修复算法。该算法在网络部署完成后利用分布式的方法选择关键节点并对其进行监控。当节点失效时,使用冗余节点选择模型选择冗余节点,通过移动冗余节点对失效节点进行修复,同时对冗余节点采取睡眠唤醒策略以延长网络寿命。实验结果表明,与区域移动修复算法相比,该算法在节点移动总距离、网络寿命、失效节点首次出现时间、投递率等方面性能均有所提高。     

一种最大化Ad Hoc网络生存期的拓扑控制算法

如何延长无线自组网生存期是拓扑控制技术研究的重点.根据无线自组网通信的特点,基于目前使用最广泛的网络生存期定义和能耗模型,综合考虑节点的发送和接收功耗,通过分析网络生存期与节点通信距离、电路损耗及节点负载量的关系,得出拓扑控制与网络生存期的关系.在此基础上提出延长网络生存期的分布式拓扑控制算法MLTC.网络中每个节点收集其邻居节点信息,分布式构建具有最长路径生存期特性的局部生成子图,并选取覆盖所有最优相邻节点的最小发射功率为此节点的发射功率.算法在保证网络连通性与无向性的同时,使得节点能用最小功率构建保存了原图最长生存期路径的子图.理论分析和仿真实验结果表明,MLTC算法在不同的发送和接收功耗比下均能有效延长网络的生存期.     

一种新的因特网拓扑的序列分析方法:dM序列分析方法

网络拓扑研究的一项重要内容是分析网络拓扑的特征并生成满足这些特征的拓扑图。拓扑图特征的dK序列分析技术是一种系统化的拓扑分析技术,它能够以不同的精度描述拓扑图的特征,随着d的增加,其生成的拓扑图能够在各种重要的拓扑度量方面越来越接近原始拓扑图,因而对因特网拓扑研究具有重要意义。dK序列分析技术的问题在于状态数较多,生成算法复杂,当d>2时没有直接的生成算法。本文提出了一种新的基于邻接图分布的拓扑图特征的序列分析技术:dM序列分析技术。与dK序列分析技术相比,dM序列分析技术具有状态数少、生成算法简单的优势,因此更适合于大规模拓扑图如因特网AS拓扑的研究。     

散列节点网络成图方法分析研究

网络管理就是维护一个网络系统的正常运行,其中给人最直观的呈现就是网络拓扑图。网络拓扑图的绘制方法有很多种,目前广泛采用的方法有基于ICMP、ARP和SNMP协议的拓扑算法,其中以基于SNMP协议的拓扑算法最为主流。对于任意网络中的散列节点如何不依赖于特定协议而自发的进行拓扑图绘制,这在各种小型网络建设中是非常重要的。本文将根据一个实例,对散列节点网络成图方法进行研究与分析。除了研究得到拓扑图的方法外还将对最终成图结果如何与实际相符合做出讨论。     

基于DHT的拓扑感知节点聚集算法

在文件共享、流媒体和协作计算等P2P应用模型中,节点间采用单播通信并构建出对应的覆盖网络.由于覆盖网络通常建立在已有的底层网络之上,节点随机加入系统将导致上下层网络拓扑不匹配,不仅增加了节点间通信延时而且给底层网络带来较大的带宽压力.当前的拓扑匹配算法尚存在可扩展性低、节点聚集时延长等问题.在网络坐标算法和DHT算法基础之上,提出一种分布式的拓扑感知节点聚集算法TANRA,利用等距同心圆簇对节点二维网络坐标平面进行等面积划分,并根据节点所处区域进行多层命名空间中区间的一一映射.由于保留了节点之间的邻近关系,从而可使用DHT基本的“发布”和“搜索”原语进行相邻节点聚集.仿真结果表明,TANRA算法在大规模节点数时能有效保证网络拓扑匹配,并且具有较低的加入延时.     

无线传感器网络网内数据处理节点的优化选取

能量是无线传感器网络至关重要的资源,数据传输占据着能耗的主体,当前,大多数研究围绕最小化传输能耗而展开.网内数据处理是选择数据传输的某一中继节点作为处理节点,利用该节点所具备的计算能力对原始数据进行处理,再将处理结果返回给接收节点,从而达到降低传输能耗的目的.网内数据处理节点的最优选取,可以最小化数据查询的传输能耗.通过建立数学模型来描述传输能耗与处理节点选取策略的定量关系,提出一种不需要全局网络拓扑信息的低能耗的处理节点选取策略(energy efficient selection strategy,简称EESS).与现有方法相比,该策略使用较少的控制开销并能显著降低数据的传输能耗.模拟实验结果表明,EESS在低密度的网络结构以及长距离的查询操作下具有良好的性能,更有利于延长无线传感器网络的寿命.     

结合极大似然距离估计的MDS-MAP节点定位算法

基于多维定标的定位算法通常利用节点间的最短路径长度代替欧式距离构建距离矩阵,当网络拓扑结构不规则时,会导致较大的定位误差。针对这一问题,提出了一种结合极大似然距离估计和多维定标的节点定位算法MDS-MAP（MLE）。算法将待测节点的一跳邻居节点信息作为极大似然方法的输入,利用与邻居节点的距离信息计算待测节点的相对坐标,然后根据已知锚节点的坐标,将所有节点的相对坐标映射为绝对坐标。实验结果表明,针对规则网络和不规则网络,MDS-MAP （MLE）算法均可取得较好的定位精度,且当网络连通度在一定范围内变化时,定位误差可保持在较低的稳定区间内。     

MIGSOM: Multilevel Interior Growing Self-Organizing Maps for High Dimensional Data Clustering

Understanding the inherent structure of high-dimensional datasets is a very challenging task. This can be tackled from visualization, summarizing or simply clustering points of view. The Self-Organizing Map (SOM) is a powerful and unsupervised neural network to resolve these kinds of problems. By preserving the data topology mapped onto a grid, SOM can facilitate visualization of data structure. However, classical SOM still suffers from the limits of its predefined structure. Growing variants of SOM can overcome this problem, since they have tried to define a network structure with no need an advance a fixed number of output units by dynamic growing architecture. In this paper we propose a new dynamic SOMs called MIGSOM: Multilevel Interior Growing SOMs for high-dimensional data clustering. MIGSOM present a different architecture than dynamic variants presented in the literature. Using an unsupervised training process MIGSOM has the capability of growing map size from the boundaries as well as the interior of the network in order to represent more faithfully the structure present in a data collection. As a result, MIGSOM can have three-dimensional (3-D) structure with different levels of oriented maps developed according to data direction. We demonstrate the potential of the MIGSOM with real-world datasets of high-dimensional properties in terms of topology preserving visualization, vectors summarizing by efficient quantization and data clustering. In addition, MIGSOM achieves better performance compared to growing grid and the classical SOM.     

A directed batch growing approach to enhance the topology preservation of self-organizing map

The growing self-organizing map (GSOM) possesses effective capability to generate feature maps and visualizing high-dimensional data without pre-determining their size. Most of the proposed growing SOM algorithms use an incremental learning strategy. The conventional growing approach of GSOM is based on filling all available position around the candidate neuron which can decrease the topology preservation quality of the map due to the misconfiguration and twisting of the map which could be a consequence of unexpected network growth and improper neuron addition and weight initialization. To overcome this problem, in this paper we introduce a batch learning strategy for growing self-organizing maps called DBGSOM which direct the growing process based on the accumulative error around the candidate boundary neuron. In the proposed growing approach, just one new neuron is added around each candidate boundary neuron. The DBGSOM offers suitable mechanisms to find a proper growing positions and allocating initial weight vectors for the new neurons.The potential of the DBGSOM was investigated with one synthetic dataset and six real-world benchmark datasets in terms of topology preservation and mapping quality. Experimental results showed that the proposed growing strategy provides an enhanced topology preserved map and reduces the susceptibility of twisting compared to GSOM. Furthermore, the proposed method has a better clustering ability than GSOM and SOM. According to the lower number of neurons generated by DBGSOM, it needs less time to learn the manifold of the data points compared to GSOM.     

An Adaptive Partitioning Scheme for Sleep Scheduling and Topology Control in Wireless Sensor Networks

This paper presents an adaptive partitioning scheme of sensor networks for node scheduling and topology control with the aim of reducing energy consumption. Our scheme partitions sensors into groups such that a connected backbone network can be maintained by keeping only one arbitrary node from each group in active status while putting others to sleep. Unlike previous approaches that partition nodes geographically, our scheme is based on the measured connectivity between pairwise nodes and does not depend on nodes' locations. In this paper, we formulate node scheduling with topology control as a constrained optimal graph partition problem, which is NP-hard, and propose a Connectivity-based Partition Approach (CPA), which is a distributed heuristic algorithm, to approximate a good solution. We also propose a probability-based CPA algorithm to further save energy. CPA can ensure K-vertex connectivity of the backbone network, which achieves the trade-off between saving energy and preserving network quality. Moreover, simulation results show that CPA outperforms other approaches in complex environments where the ideal radio propagation model does not hold.     

构造基于信任机制的自组织资源拓扑

利用P2P覆盖网络(P2P overlay networks)进行资源组织是当前研究的热点,如何保证资源获取的可靠性是研究人员面临的一个主要问题.利用节点的动态自组织属性,基于节点物理位置的拓扑构造可以提高P2P网络的性能,但没有关注P2P网络中恶意节点的问题;基于偏好的拓扑构造可以有效地提高资源共享和搜索的效率,但没有考虑节点实际提供服务的能力和节点行为的可靠性.提出了一个基于信任机制的自组织资源拓扑构造方案,利用Bayesian方法根据节点的行为来评估节点的信任度,通过节点间基于信任关系的链路更新,构造出新的自组织拓扑结构.仿真实验表明,该拓扑结构不仅有利于节点发现资源的效率,提高整个P2P网络的交互性能,还能使节点聚集在服务能力较强的可信节点周围,保证资源选取的可靠性.     

基于移动Agent的无线传感器网络拓扑发现机制

对监测区域中部署的传感器节点的拓扑发现是传感器网络应用的前提,它反映了传感器网络的监测能力。考虑目前拓扑发现算法中能量消耗过多、网络连通性不强等问题,文中结合移动Agent的特点,提出了一种基于移动Agent的无线传感器网络拓扑发现机制,通过建立数学模型,利用相关邻近图（relative neighborhood graph）理论生成网络拓扑。实验结果表明,基于移动Agent的拓扑发现机制相对于当前存在的拓扑发现算法具有很好的稳定性和良好的节能效果,该算法可以解决节点拓扑请求信息讨多导致过多能量消耗的问颢．     

基于对抗图卷积的网络表征学习框架

现有的网络表征方法及其相关变体的侧重点在于保存网络的拓扑结构或使重构误差最小,忽略隐变量的数据分布问题.基于此种情况,文中提出基于对抗图卷积的网络表征学习框架(AGCN),使网络模型不仅可以组合图的结构信息和节点的属性信息,提高网络表征学习性能,而且可以学习数据分布.与此同时,在AGCN的基础上提出端到端的多任务学习框架(MTL),在一个学习阶段可以同时进行链接预测和节点分类任务.实验表明,MTL性能较优.     

A combined measure for quantifying and qualifying the topology preservation of growing self-organizing maps

The Self-Organizing Map (SOM) is a neural network model that performs an ordered projection of a high dimensional input space in a low-dimensional topological structure. The process in which such mapping is formed is defined by the SOM algorithm, which is a competitive, unsupervised and nonparametric method, since it does not make any assumption about the input data distribution. The feature maps provided by this algorithm have been successfully applied for vector quantization, clustering and high dimensional data visualization processes. However, the initialization of the network topology and the selection of the SOM training parameters are two difficult tasks caused by the unknown distribution of the input signals. A misconfiguration of these parameters can generate a feature map of low-quality, so it is necessary to have some measure of the degree of adaptation of the SOM network to the input data model. The topology preservation is the most common concept used to implement this measure. Several qualitative and quantitative methods have been proposed for measuring the degree of SOM topology preservation, particularly using Kohonen's model. In this work, two methods for measuring the topology preservation of the Growing Cell Structures (GCSs) model are proposed: the topographic function and the topology preserving map.     

Convolutional neural network feature maps selection based on LDA

Convolutional neural network (CNN), as widely applied to vision and speech, has developed lager and lager network size in last few years. In this paper, we propose a CNN feature maps selection method which can simplify CNN structure on the premise of stabilize the classifier performance. Our approach aims to cut the feature map number of the last subsampling layer and achieves shortest runtime on the basis of Linear Discriminant Analysis (LDA). We rebuild feature maps selection formula based on the between-class scatter matrix and within-class scatter matrix, because LDA can lead to information loss in the dimension-reduction process. Our experiments measure on two standard datasets and a dataset made by ourselves. According to the separability value of each feature map, we suggest the least number of feature maps which can keep the classifier performance. Furthermore, we prove that separability value is an effective indicator for reference to select feature maps.