分布式空间信息的对等协同混合发现模型*

海量空间信息的处理需要分布式协同工作的GIS平台的支持,为了解决经典的分散式结构化的分布式哈希表逻辑网络结构增加的延时和在构建哈希表的过程中逻辑覆盖网络往往和物理网络不一致的问题,提出一种分布式空间信息的对等协同混合发现模型。基于空间资源发现代理节点和普通邻居节点,该模型实现了集中式的全局空间资源发现模型与分散式结构化的分布式哈希表模型之间的自动切换,能够自适应地调整空间资源的逻辑网络结构以提供更好的性能。基于节点交换机制,设计了构建路由表和降低延时的算法,通过发现有利于覆盖网络和物理网络匹配的节点交换来     

Peer-to-peer information retrieval using shared-content clustering

Peer-to-peer (p2p) networks are used by millions for searching and downloading content. Recently, clustering algorithms were shown to be useful for helping users find content in large networks. Yet, many of these algorithms overlook the fact that p2p networks follow graph models with a power-law node degree distribution. This paper studies the obtained clusters when applying clustering algorithms on power-law graphs and their applicability for finding content. Driven by the observed deficiencies, a simple yet efficient clustering algorithm is proposed, which targets a relaxed optimization of a minimal distance distribution of each cluster with a size balancing scheme. A comparative analysis using a song-similarity graph collected from 1.2 million Gnutella users reveals that commonly used efficiency measures often overlook search and recommendation applicability issues and provide the wrong impression that the resulting clusters are well suited for these tasks. We show that the proposed algorithm performs well on various measures that are well suited for the domain.     

Building Small-World Peer-to-Peer Networks Based on Hierarchical Structures

Small-world (SW) networks possess two properties, namely low diameter and high clustering coefficient, which are often desired by large-scale peer-to-peer networks. Prior studies have shown that the construction of an SW network can be based on a d-regular graph, and each node in the graph maintains d local neighbors and a small constant number of long-distance contacts. However, it is commonly understood that it is difficult to construct a short route in an SW network, given source (s) and target (t) nodes, though an SW network guarantees that a short route from s to t exists. Prior work in [1] proposed a navigable SW network for a d-dimensional lattice such that a simple localized routing algorithm can be devised to route a message from s to t using O(log^{2}{cal X}) hops, where {cal X} is the number of nodes in the network. In this paper, we present a novel navigable SW network based on a hierarchical model. Compared to previous efforts, the novelty of our study presents 1) that our network construction based on a hierarchical model is decentralized, 2) that routing a message between any two nodes in our SW network takes logarithmic hopcount in expectation, 3) that our SW network has high cluster coefficient, and 4) that the performance of our proposal is mathematically provable. We support the performance of our proposal in this study through rigorous, thorough performance analysis and extensive simulations.     

Adding structure to unstructured peer-to-peer networks: the use of small-world graphs

The “small-world” graph structure is pervasive and is observed to arise “without-design” or “naturally” in many practical systems such as the World Wide Web. In contrast to natural systems, overlay networks provide an opportunity to design structure. We seek the advantages of designing overlay topologies with small-world properties to support file sharing in peer-to-peer networks. We focus on two metrics of performance: (a) search protocol performance, a local gain perceived directly by peer-to-peer network users and (b) network utilization, a global property that is of interest to network service providers. We propose a class of overlay topologies and show, by simulation, that a particular topology instance of this class where every node has many close neighbors and few random neighbors (i.e., a small-world graph) exhibits very good properties. In this overlay topology, the chances of locating files are high, and the nodes where these files are found are, on average, close to the query source. This improvement in search protocol performance is achieved while decreasing the traffic load on the links in the underlying network. We propose a simple greedy algorithm to construct such overlay topologies where each node operates independently and in a decentralized manner to select its neighbors.     

Managing peer-to-peer networks with human tactics in social interactions

Small-world phenomena have been observed in existing peer-to-peer (P2P) networks which has proved useful in the design of P2P file-sharing systems. Most studies of constructing small world behaviours on P2P are based on the concept of clustering peer nodes into groups, communities or clusters. However, managing additional multilayer topology increases maintenance overhead, especially in highly dynamic environments. In this paper, we present Social-like P2P systems (Social-P2Ps) for object discovery by self-managing P2P topology with human tactics in social networks. In Social-P2Ps, queries are routed intelligently even with limited cached knowledge and node connections. Unlike community-based P2P file-sharing systems, we do not intend to create and maintain peer groups or communities consciously. In contrast, each node connects to other peer nodes with the same interests spontaneously by the result of daily searches.     

具有逻辑路由跳数O(1)的可扩展的分散式查找算法

对等网络中的一个基本问题就是如何高效地进行数据查找．分散式查找是解决这类问题的一种新思路．现有的分散式查找方法在查找时所需的逻辑路由跳数都与网络中的节点数相关(一般为O(logn)，少数为O(n^1/c)．Sifter是一种可扩展、自组织、高容错和高效率的分散式查找算法．在该算法中，单个节点只需维护O(n1／c)个其他节点的链接信息，就能够在O(1)个逻辑路由跳内找到目的数据．该算法适用于网络动态性不大，但是对查找的实时性要求较高的应用．     

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.     

一种适用于移动对等网络的分簇算法

通过分簇算法减小网络振动效应,延长网络的寿命是移动对等网络的研究重点之一。在研究Kautz图及其特性的基础上,提出一种基于Kautz图的移动对等网络分簇算法。在算法中,定义地址空间树,使用Kautz串作为节点标识,并运用后根序和宽度优先算法遍历地址空间树等一系列技术生成簇。同时设计了相关机制管理和维护簇结构,保证结构一致性。理论证明和实验评估表明,该分簇算法能有效减小振动效应,延长网络寿命。     

无线传感器网络拓扑三级分簇优化算法

针对大规模分布式传感器网络提出一种拓扑三级分簇结构优化算法. 通过引入传感器休眠模式, 并考虑到分簇数目较多的情况, 对多个簇头节点采用生成最小刚性图的方法进行拓扑优化, 以实现传感器网络整体能量均衡,使传感器网络具有较好的连通性和鲁棒性. 仿真实验表明, 与已有相关算法相比, 采用所提出的算法可使网络延缓出现节点死亡现象, 有利于实现网络负载均衡, 并且网络中节点整体存活时间较长, 从而延长网络的生命周期.

     

P2P网络中的社区结构发现方法

针对P2P网络的分布式特征,提出了一种可扩展的分布式社区发现方法PDC,采用节点Power值度量社区结构,并选择中心节点,其中包括新的社区度量方法和新的中心节点选择算法。仿真实验结果证明,与FDC和CDC方法相比,和其他算法相比PDC的社区发现效果率比FDC和CDC至少提高了510%,但是产生的消息数量却很少比FDC少一个数量级,具有良好的可扩展性。     

Sensor Network Structure Recognition Based on P-law

A sensor graph network is a sensor network model organized according to graph network structure. Structural unit and signal propagation of core nodes are the basic characteristics of sensor graph networks. In sensor networks, network structure recognition is the basis for accurate identification and effective prediction and control of node states. Aiming at the problems of difficult global structure identification and poor interpretability in complex sensor graph networks, based on the characteristics of sensor networks, a method is proposed to firstly unitize the graph network structure and then expand the unit based on the signal transmission path of the core node. This method which builds on unit patulousness and core node signal propagation (called p-law) can rapidly and effectively achieve the global structure identification of a sensor graph network. Different from the traditional graph network structure recognition algorithms such as modularity maximization and spectral clustering, the proposed method reveals the natural evolution process and law of graph network subgroup generation. Experimental results confirm the effectiveness, accuracy and rationality of the proposed method and suggest that our method can be a new approach for graph network global structure recognition.     

Automated sub-zoning of water distribution systems

Water distribution systems (WDS) are complex pipe networks with looped and branching topologies that often comprise thousands to tens of thousands of links and nodes. This work presents a generic framework for improved analysis and management of WDS by partitioning the system into smaller (almost) independent sub-systems with balanced loads and minimal number of interconnections. This paper compares the performance of three classes of unsupervised learning algorithms from graph theory for practical sub-zoning of WDS: (1) Global clustering – a bottom-up algorithm for clustering n objects with respect to a similarity function, (2) Community structure – a bottom-up algorithm based on the property of network modularity, which is a measure of the quality of network partition to clusters versus randomly generated graph with respect to the same nodal degree, and (3) Graph partitioning – a flat partitioning algorithm for dividing a network with n nodes into k clusters, such that the total weight of edges crossing between clusters is minimized and the loads of all the clusters are balanced. The algorithms are adapted to WDS to provide a practical decision support tool for water utilities. Visual qualitative and quantitative measures are proposed to evaluate models' performance. The three methods are applied for two large-scale water distribution systems serving heavily populated areas in Singapore.     

Perceptual organization in user-generated graph layouts

Many graph layout algorithms optimize visual characteristics to achieve useful representations. Implicitly, their goal is to create visual representations that are more intuitive to human observers. In this paper, we asked users to explicitly manipulate nodes in a network diagram to create layouts that they felt best captured the relationships in the data. This allowed us to measure organizational behavior directly, allowing us to evaluate the perceptual importance of particular visual features, such as edge crossings and edge-lengths uniformity. We also manipulated the interior structure of the node relationships by designing data sets that contained clusters, that is, sets of nodes that are strongly interconnected. By varying the degree to which these clusters were "masked" by extraneous edges we were able to measure observers' sensitivity to the existence of clusters and how they revealed them in the network diagram. Based on these measurements we found that observers are able to recover cluster structure, that the distance between clusters is inversely related to the strength of the clustering, and that users exhibit the tendency to use edges to visually delineate perceptual groups. These results demonstrate the role of perceptual organization in representing graph data and provide concrete recommendations for graph layout algorithms.     

Robust self-stabilizing weight-based clustering algorithm

Ad hoc networks consist of wireless hosts that communicate with each other in the absence of a fixed infrastructure. Such networks cannot rely on centralized and organized network management. The clustering problem consists of partitioning network nodes into non-overlapping groups called clusters. Clusters give a hierarchical organization to the network that facilitates network management and that increases its scalability.In a weight-based clustering algorithm, the clusterheads are selected according to their weight (a node’s parameter). The higher the weight of a node, the more suitable this node is for the role of clusterhead. In ad hoc networks, the amount of bandwidth, memory space or battery power of a node could be used to determine weight values.A self-stabilizing algorithm, regardless of the initial system configuration, converges to legitimate configurations without external intervention. Due to this property, self-stabilizing algorithms tolerate transient faults and they are adaptive to any topology change.In this paper, we present a robust self-stabilizing weight-based clustering algorithm for ad hoc networks. The robustness property guarantees that, starting from an arbitrary configuration, after one asynchronous round, the network is partitioned into clusters. After that, the network stays partitioned during the convergence phase toward a legitimate configuration where the clusters verify the “ad hoc clustering properties”.     

Distributed clustering algorithms for data-gathering in wireless mobile sensor networks

One critical issue in wireless sensor networks is how to gather sensed information in an energy-efficient way since the energy is a scarce resource in a sensor node. Cluster-based architecture is an effective architecture for data-gathering in wireless sensor networks. However, in a mobile environment, the dynamic topology poses the challenge to design an energy-efficient data-gathering protocol. In this paper, we consider the cluster-based architecture and provide distributed clustering algorithms for mobile sensor nodes which minimize the energy dissipation for data-gathering in a wireless mobile sensor network. There are two steps in the clustering algorithm: cluster-head election step and cluster formation step. We first propose two distributed algorithms for cluster-head election. Then, by considering the impact of node mobility, we provide a mechanism to have a sensor node select a proper cluster-head to join for cluster formation. Our clustering algorithms will achieve the following three objectives: (1) there is at least one cluster-head elected, (2) the number of cluster-heads generated is uniform, and (3) all the generated clusters have the same cluster size. Last, we validate our algorithms through an extensive experimental analysis with Random Walk Mobility (RWM) model, Random Direction Mobility (RDM) model, and a Simple Mobility (SM) model as well as present our findings.     

EEHC: Energy efficient heterogeneous clustered scheme for wireless sensor networks

In recent years, there has been a growing interest in wireless sensor networks. One of the major issues in wireless sensor network is developing an energy-efficient clustering protocol. Hierarchical clustering algorithms are very important in increasing the network’s life time. Each clustering algorithm is composed of two phases, the setup phase and steady state phase. The hot point in these algorithms is the cluster head selection. In this paper, we study the impact of heterogeneity of nodes in terms of their energy in wireless sensor networks that are hierarchically clustered. We assume that a percentage of the population of sensor nodes is equipped with the additional energy resources. We also assume that the sensor nodes are randomly distributed and are not mobile, the coordinates of the sink and the dimensions of the sensor field are known. Homogeneous clustering protocols assume that all the sensor nodes are equipped with the same amount of energy and as a result, they cannot take the advantage of the presence of node heterogeneity. Adapting this approach, we introduce an energy efficient heterogeneous clustered scheme for wireless sensor networks based on weighted election probabilities of each node to become a cluster head according to the residual energy in each node. Finally, the simulation results demonstrate that our proposed heterogeneous clustering approach is more effective in prolonging the network lifetime compared with LEACH.     

基于适应度的簇划分算法研究

延长网络生存期、减少网络能量消耗是传感器网络一项重要性能指标,分簇方案是实现该目标的主要方法之一.通过分析影响簇状网能耗的主要能耗参数,引入节点适应度模型,并该模型运用到簇划分算法中,优化网络能量效率.算法是分布式簇划分算法,在簇划分过程中,通过比较节点局部区域能量比、通信代价比、节点度数综合能耗因素,决定簇首节点和成簇规模.仿真结果表明该算法能够降低簇间的通信重叠,均衡网络负载,与几种算法比较适应度分簇算法使网络生存时间增加的幅度虽然不是很大,但是使网络系统的稳定时间比其他2种能量有效的算法延长了近20%左右.     

移动环境中一种基于Hash的P2P覆盖网

目前提出的大多数基于哈希（hash—based）的P2P网络都集中于固定的对等节点。当节点移动到网络中一个新的位置时,这种结构在消息传递等方面的效率就会下降。文章提出一种移动环境中的基于哈希的P2P覆盖网（Hash—based P2P Overlay in Mobile Environment,H—MP2P）,允许节点在网络中自由移动。一个节点可通过P2P网络广播其位置信息,其他节点通过网络可以获知该节点的移动信息并进行定位。通过理论分析和实验可知H—MP2P在扩展性、可靠性和效率方面都可以取得较好的结果,可以很好的应用在移动环境中。     

一种考虑障碍物的ad hoc网络分簇算法

现有的很多ad hoc网络分簇算法都没有考虑实际的物理环境因素,如地球表面的各种障碍物。而障碍物既阻碍节点移动,又限制无线传输,对分簇结果影响很大,可能会导致簇的尺寸过小,簇的数目较多,从而引入大量的通信和计算开销。结合Voronoi图,在最小ID启发式算法的基础上,提出一种考虑障碍物的分簇算法。通过设置备用节点,可以解决障碍物环境下ad hoc网络的连接性问题。最后通过实例仿真对该算法和最小ID算法进行性能比较和评价。     

动态组簇的协同定位算法

提出一种动态组簇的协同定位方法,用于基于传感器网络的目标定位和跟踪.该方法包括数据融合算法和虚拟簇漂移(virtual cluster shift,VCS)机制两部分.数据融合算法部分采用均值漂移(mean shift)算法.虚拟簇漂移机制分布式地在组织目标周围的锚节点建立临时簇.簇首管理簇成员,收集感知数据,执行融合算法.当虚拟簇无法锁定目标时,簇首指定离目标最近的簇成员担任新簇首,簇的成员也进行更替,由此将虚拟簇移动(shift)到合适的位置.分析和仿真结果显示,采用动态组簇的协同定位方法跟踪目标可以大幅度降低通信开销,产生的通信量仅为以往集中式定位算法开销的1/3.