一种节能的无线传感器网络路由协议的设计与实现

在无线传感器网络的路由协议中,基于簇的路由协议在拓扑管理、能量利用、数据融合等方面具有优势。本文针对目前已有协议能量消耗大、网络寿命短等问题,提出了一种能量感知的基于分布式簇算法的无线传感器网络协议EA-HEED。此协议改进了分布式的簇头选举算法,分配时分复用时隙并在簇头节点建立一棵路由树,从而提高簇头选举效率;设计了休眠冗余节点的簇内活动节点调度算法,减少能耗;采用考虑节点能量和节点与基站距离的簇头节点组织路由树方法、最小化网络开销以及能量负载平衡方法,优化路由协议,有效延长网络寿命。仿真结果表明,与LEACH和HEED协议相比,EAHEED协议可以进一步延长网络寿命。     

无线传感器网络中一种能量均衡的分布式成簇算法

分簇算法是无线传感器网络路由算法研究的主要方向之一.为了解决分簇算法中网络节点能量负载不平衡的问题,提出了一种能量均衡的分布式成簇算法.算法采用簇头轮转方法,并在每轮成簇过程中,每个节点基于所在簇的局部信息评估自身的能量水平,用以确定自己在下一轮的阈值,从而相应地调整其出任簇头的概率,实现整个网络的能量消耗更加均衡,最大限度地延长网络生命周期的目的.仿真实验结果表明,新的分簇算法能量均衡性更好,能提供更长的网络生命周期和更高的数据精度.     

无线传感器网络中分布式多跳路由算法研究

在对无线传感器网络路由算法深入研究的基础上,设计出了一种完全分布式的、能量有效的无线传感器网络多跳路由算法,主要内容包括:(1)在成簇方面,给出了一种基于时间延迟机制的无线传感器网络成簇算法CHTD,解决了相同能量节点在产生簇头时的碰撞问题。并通过仿真验证了CHTD成簇算法比LEACH和目前已有的基于定时器的成簇算法TB-LEACH对网络性能有明显改善;(2)在簇头数据传输方面,给出CHTD-M簇间多跳路由算法。该算法将网络中均匀分布的簇头构造成一棵路由树,通过多跳传输的方式减少直接与基站通信的簇头节点数量。最后对整体算法进行仿真,实验结果表明,CHTD-M把节约网络能量和保持网络负载平衡很好的结合起来,显著地延长了网络的生命周期。     

无线传感器网络中一种混合动态自适应路由算法

针对无线传感器网络的节能以及能耗均衡问题,本文提出了一种无线传感器网络混合路由网络模型,将平面路由和层次路由有机地结合在一起,在数据获取阶段采用层次路由,而在数据传输过程中使用平面路由。同时,论文提出了一种基于该模型的动态成簇自适应路由算法HDAR（Hybrid Dynamic Adaptive Routing algorithm）。在算法中设计了基于现场数据的动态成簇机制来完成数据的收集,使用自适应的路由选择算法将数据传输回Sink节点。仿真结果表明HDAR协议在节能和能耗均衡方面达到了良好的效果。     

一种基于节点位置和密度的非均匀分簇路由算法

分析了现有分簇路由算法,提出了基于节点位置和密度的非均匀分簇路由算法。簇头选举阶段,考虑了节点的剩余能量,并引入竞争机制进行簇头选择;成簇阶段,综合考虑节点与基站的距离、节点密度以进行非均匀分簇,达到节点能耗均衡的效果,同时解决路由热区问题;簇间路由阶段,通过设立通信簇头节点,使簇间数据转发任务从簇头中分离,簇头节点只负责簇内的数据收集和融合,而通信簇头节点负责簇间数据传输,减少了簇头的能量消耗。实验结果表明,改进后的路由算法能够有效地均衡网络负载,并显著地延长网络的生命周期。     

无线传感器网络的成簇算法

如何合理、有效地利用成簇算法使得网络节点具有均衡的负载和较小能耗率成为当前无线传感器网络研究领域的热点问题之一。根据无线传感器网络的分簇机制,着重从簇首的选举、簇组织和簇的路由三个方面系统地分析了当前典型的成簇算法,对算法的特点和适用情况进行了比较分析,并指出了目前算法存在的问题和需要进一步研究的内容。     

基于LEACH的无线传感器网络分簇路由算法

路由协议是无线传感器网络的重要组成部分之一,而路由算法在路由协议中起着至关重要的作用。文章在LEACH算法基础上,提出一种改进的路由算法,改进后的算法采用相对固定的成簇方式,每隔一轮重新构建簇。利用图论中的prim算法,选择每轮中Ped最大的簇头作为根节点,在簇头节点之间构造树形路由,簇头之间以多跳方式将收集到的数据发送到根节点,然后通过根节点将整个网络收集到的数据发送到基站。仿真结果表明,与LEACH算法相比,改进算法降低了能耗,有效延长了网络生存周期。     

无线传感器网络自适应动态路由算法仿真

传统传感器路由算法存在信息传输能耗较高,且网络节点存活率偏低问题,提出无线传感器网络自适应动态路由算法,简称为HDAR算法.结合平面路由和层次路由构建新的无线传感器网络路由框架,在数据获取模块中选取层次路由,在数据传输模块中选取平面路由.调整节点非线性自适应权重,动态成簇自适应路由算法HDAR通过数据动态成簇来实现数据汇总,利用自适应路由选择算法将数据运转到Sink节点,最终实现HDAR算法设计.为验证所提算法的有效性,进行一次实验.实验结果表明:HDAR算法节能效果更好,且上述算法下节点存活数量更多,适用性较强,具有很好的应用前景.     

无线传感器网络成簇路由算法研究

无线传感器网络被用于很多应用中,已经成为无线网络研究的重点方向.为了得到广泛分布于空间节点的感知信息,需要为传感器网络提供可靠的传输路由.本文提出了无线传感器网络的分层架构,分析了网络中成簇路由的形成过程,比较了成簇路由对应平面路由的优势,最后介绍了典型的成簇路由算法.     

三维无线自组网络的成簇及自适应路由算法

针对三维无线自组织网络拓扑结构复杂导致的不易寻路的问题,提出成簇算法和基于部分超立方体网络结构(PCCN)的自适应路由算法.成簇算法考虑到节点疏密不均的情况,利用节点的空间密度分布将节点分割成候选簇,采用融合机制将候选簇构建成更均匀的簇结构.使用实际拓扑到虚拟拓扑的转化策略,在簇结构的基础上构建PCCN.PCCN作为虚拟拓扑结构,简化了实际网络拓扑,具有可扩展性、延伸性能好等优点.利用PCCN,对节点进行编号之后进行自适应路由.自适应路由算法包括簇内和簇间路由两种情况.算法分析及算例表明,PCCN简化了三维网络的拓扑结构,能够有效路由,为三维自组织网络的管理提出了新的方法和手段.     

Network regression with predictive clustering trees

Network data describe entities represented by nodes, which may be connected with (related to) each other by edges. Many network datasets are characterized by a form of autocorrelation, where the value of a variable at a given node depends on the values of variables at the nodes it is connected with. This phenomenon is a direct violation of the assumption that data are independently and identically distributed. At the same time, it offers an unique opportunity to improve the performance of predictive models on network data, as inferences about one entity can be used to improve inferences about related entities. Regression inference in network data is a challenging task. While many approaches for network classification exist, there are very few approaches for network regression. In this paper, we propose a data mining algorithm, called NCLUS, that explicitly considers autocorrelation when building regression models from network data. The algorithm is based on the concept of predictive clustering trees (PCTs) that can be used for clustering, prediction and multi-target prediction, including multi-target regression and multi-target classification. We evaluate our approach on several real world problems of network regression, coming from the areas of social and spatial networks. Empirical results show that our algorithm performs better than PCTs learned by completely disregarding network information, as well as PCTs that are tailored for spatial data, but do not take autocorrelation into account, and a variety of other existing approaches.     

Flocking based approach for data clustering

Data clustering is a process of extracting similar groups of the underlying data whose labels are hidden. This paper describes different approaches for solving data clustering problem. Particle swarm optimization (PSO) has been recently used to address clustering task. An overview of PSO-based clustering approaches is presented in this paper. These approaches mimic the behavior of biological swarms seeking food located in different places. Best locations for finding food are in dense areas and in regions far enough from others. PSO-based clustering approaches are evaluated using different data sets. Experimental results indicate that these approaches outperform K-means, K-harmonic means, and fuzzy c-means clustering algorithms.     

Soft clustering of multidimensional data: a semi-fuzzy approach

This paper discusses new approaches to unsupervised fuzzy classification of multidimensional data. In the developed clustering models, patterns are considered to belong to some but not necessarily all clusters. Accordingly, such algorithms are called ‘semi-fuzzy’ or ‘soft’ clustering techniques. Several models to achieve this goal are investigated and corresponding implementation algorithms are developed. Experimental results are reported.     

Clustering high dimensional data: A graph-based relaxed optimization approach

There is no doubt that clustering is one of the most studied data mining tasks. Nevertheless, it remains a challenging problem to solve despite the many proposed clustering approaches. Graph-based approaches solve the clustering task as a global optimization problem, while many other works are based on local methods. In this paper, we propose a novel graph-based algorithm “GBR” that relaxes some well-defined method even as improving the accuracy whilst keeping it simple. The primary motivation of our relaxation of the objective is to allow the reformulated objective to find well distributed cluster indicators for complicated data instances. This relaxation results in an analytical solution that avoids the approximated iterative methods that have been adopted in many other graph-based approaches. The experiments on synthetic and real data sets show that our relaxation accomplishes excellent clustering results. Our key contributions are: (1) we provide an analytical solution to solve the global clustering task as opposed to approximated iterative approaches; (2) a very simple implementation using existing optimization packages; (3) an algorithm with relatively less computation time over the number of data instances to cluster than other well defined methods in the literature.     

A mathematical morphology based method for hierarchical clustering analysis of spatial points on street networks

Spatial clustering analysis is an important issue that has been widely studied to extract the meaningful subgroups of geo-referenced data. Although many approaches have been developed in the literature, efficiently modeling the network constraint that objects (e.g. urban facility) are observed on or alongside a street network remains a challenging task for spatial clustering. Based on the techniques of mathematical morphology, this paper presents a new spatial clustering approach NMMSC designed for mining the grouping patterns of network-constrained point objects. NMMSC is essentially a hierarchical clustering approach, and it generally consists of two main steps: first, the original vector data is converted to raster data by utilizing basic linear unit of network as the pixel in network space; second, based on the specified 1-dimensional raster structure, an extended mathematical morphology operator (i.e. dilation) is iteratively performed to identify spatial point agglomerations with hierarchical structure snapped on a network. Compared to existing methods of network-constrained hierarchical clustering, our method is more efficient for cluster similarity computation with linear time complexity. The effectiveness and efficiency of our approach are verified through the experiments with real and synthetic data sets.     

Combining supervised and unsupervised learning for data clustering

Clustering aims to partition a data set into homogenous groups which gather similar objects. Object similarity, or more often object dissimilarity, is usually expressed in terms of some distance function. This approach, however, is not viable when dissimilarity is conceptual rather than metric. In this paper, we propose to extract the dissimilarity relation directly from the available data. To this aim, we train a feedforward neural network with some pairs of points with known dissimilarity. Then, we use the dissimilarity measure generated by the network to guide a new unsupervised fuzzy relational clustering algorithm. An artificial data set and a real data set are used to show how the clustering algorithm based on the neural dissimilarity outperforms some widely used (possibly partially supervised) clustering algorithms based on spatial dissimilarity.     

基于K均值聚类的分段样本数据选择方法

为了提高神经网络分类器的性能,提出一种基于K均值聚类的分段样本数据选择方法.首先通过K均值聚类把训练样本根据已知的类别数进行聚类,对比聚类前后的各类样本,找出聚类错误的样本集和聚类正确的样本集;聚类正确的样本集根据各样本到聚类中心的距离进行排序并均分为五段,挑选各类的奇数段样本和聚类错误的样本构成新的训练样本集.该方法能够提取信息量大的样本,剔除冗余样本,减少样本数量的同时提高样本质量.利用该方法,结合人工和UCI数据集对三种不同的神经网络分类器进行了仿真实验,实验结果显示在训练样本平均压缩比为66.93％的前提下,三种神经网络分类器的性能都得到了提高.     

Self-organizing map network as an interactive clustering tool — An application to group technology

The Self-Organizing Map (SOM) network, a variation of neural computing networks, is a categorization network developed by Kohonen. The theory of the SOM network is motivated by the observation of the operation of the brain. This paper presents the technique of SOM and shows how it may be applied as a clustering tool to group technology. A computer program for implementing the SOM neural networks is developed and the results are compared with other clustering approaches used in group technology. The study demonstrates the potential of using the Self-Organizing Map as the clustering tool for part family formation in group technology.     

基于IRC日志的分享型社区结构的研究

用户为导向的在线分享型社区近几年发展迅速,而这些社区与以文字信息为基础的传统社区有较大的不同。利用IRC音乐频道从2001年到2006年的用户日志数据,构建了此共享型社区的用户网络。在对此网络的特点分析之后,将该网络的结构由有向的加权图转换为单向的二部图,从而利用层级聚类的方法,对网络中的用户群落进行了发掘。在此基础上,进一步分析了用户群落的大小,形状和分布的情况。最后根据研究的结果,提出了针对该社区提高服务的方法。     

结合内容相似性和时序性的社会网络挖掘

在分析聊天数据时序性的基础上,引入内容相似性信息,提出一种结合内容相似性和时序性的社会网络挖掘新方法。该方法使用启发式规则初步推断出聊天室的社会网络,利用相似用户聚类技术进一步补充并最终挖掘出准确的社会网络。实验结果显示,该方法具有较好的挖掘效果。