Ad hoc网络拓扑发现算法综述

Ad hoc是一种无线自组织多跳网络。采用无中心的分布式控制,无线节点或终端相互合作而成,独立于固定的基础设施。各节点平等、无中心,动态拓扑。网络拓扑对网络规划、仿真、管理都有非常重要的意义。针对Ad hoc拓扑结构频繁、快速变化的特点,本文主要提出了N种移动自组网络拓扑发现的算法,并基于笔者的实践提供了一个简单的算法实现,该算法主要针对同一个管理机构下的IP网络的拓扑自动发现,更复杂的拓扑发现算法可在此基础上进一步扩展。     

Simultaneous optimization of topology and geometry of flow networks

We consider a problem of minimizing a pressure drop due to a flow of a fluid or gas through an interconnected system of pipes. We show that simultaneous optimization of pipe diameters and nodal positions (topology and geometry optimization) results for fluid networks in a better optimization strategy than traditionally used ground-structure approach (topology optimization only), as opposed to the linear truss case. We also show how the results for linear flow networks can be easily carried out to nonlinear transient and turbulent cases. Stating the optimality conditions for the latter types of flow, we give an alternative derivation of generalized Murray’s law, which is well-known in physiology. We illustrate our theoretical findings with numerical examples.     

Determination of the topology of a directed network

We consider strongly-connected, directed networks of identical synchronous, finite-state processors with in- and out-degree uniformly bounded by a network constant. Via a straightforward extension of Ostrovsky and Wilkerson's Backwards Communication Algorithm [Proc. 14th Annual Symp. on Principles of Distributed Computing, 1995], we exhibit a protocol which solves the Global Topology Determination Problem, the problem of having a root processor map the global topology of a network of unknown size and topology, with running time O(ND) where N represents the number of processors and D represents the diameter of the network. A simple counting argument suffices to show that the Global Topology Determination Problem has time-complexity which makes the protocol presented asymptotically time-optimal for many large networks.     

Decentralized estimation of regression coefficients in sensor networks

Consider a wireless sensor network with a fusion center deployed to estimate a common non-random parameter vector. Each sensor obtains a noisy observation vector of the non-random parameter vector according to a linear regression model. The observation noise is correlated across the sensors. Due to power, bandwidth and complexity limitations, each sensor linearly compresses its data. The compressed data from the sensors are transmitted to the fusion center, which linearly estimates the non-random parameter vector. The goal is to design the compression matrices at the sensors and the linear unbiased estimator at the fusion center such that the total variance of the estimation error is minimized. In this paper, we provide necessary and sufficient conditions for achieving the performance of the centralized best linear unbiased estimator. We also provide the optimal compression matrices and the optimal linear unbiased estimator when these conditions are satisfied. When these conditions are not satisfied, we propose a sub-optimal algorithm to determine the compression matrices and the linear unbiased estimator. Simulation results are provided to illustrate the effectiveness of the proposed algorithm.     

Machine fusion to enhance the topology preservation of vector quantization artificial neural networks

Artificial neural networks techniques have been successfully applied in vector quantization (VQ) encoding. The objective of VQ is to statistically preserve the topological relationships existing in a data set and to project the data to a lattice of lower dimensions, for visualization, compression, storage, or transmission purposes. However, one of the major drawbacks in the application of artificial neural networks is the difficulty to properly specify the structure of the lattice that best preserves the topology of the data. To overcome this problem, in this paper we introduce merging algorithms for machine-fusion, boosting-fusion-based and hybrid-fusion ensembles of SOM, NG and GSOM networks. In these ensembles not the output signals of the base learners are combined, but their architectures are properly merged. We empirically show the quality and robustness of the topological representation of our proposed algorithm using both synthetic and real benchmarks datasets.     

Decision models for designing and planning private communication networks

We consider the recently developed reconfigurable digital data networks consisting of T1/T3 circuits and Digital Crossconnect Systems (DCSs). A DCS is a device to patch base channels electronically from one T1/T3 circuit to another with a negligible queuing delay at the connecting node. We present new decision models for the design and circuit leasing policies of such digital backbone networks. Our model takes advantage of the special capabilities of the DCS technology and is likely to result in remarkable economic gains for the private network users. The formulation and analyses presented here simultaneously address the following problems: physical link and capacity selection, logical network configuration and channel assignment, and traffic routing on the logical network. The problem formulation results in a large-scale non-linear mixed integer program, and we propose an efficient solution methodology employing Lagrangean relaxation and subgradient optimization. Several numerical results illustrate the utility of our approach for these complex problems. We show that the economies of scale built into the tariff structure of these digital networks can be successfully exploited, and that the inherent flexibility of DCSs leads to logical networks that are dramatically different from their underlying physical topologies.     

Feed-forward neural networks and topographic mappings for exploratory data analysis

A recent novel approach to the visualisation and analysis of datasets, and one which is particularly applicable to those of a high dimension, is discussed in the context of real applications. A feed-forward neural network is utilised to effect a topographic, structure-preserving, dimension-reducing transformation of the data, with an additional facility to incorporate different degrees of associated subjective information. The properties of this transformation are illustrated on synthetic and real datasets, including the 1992 UK Research Assessment Exercise for funding in higher education. The method is compared and contrasted to established techniques for feature extraction, and related to topographic mappings, the Sammon projection and the statistical field of multidimensional scaling.     

QoS vs. energy: A traffic-aware topology management scheme for green heterogeneous networks

Increasing environmental awareness combined with the high energy prices has driven the network operators to reduce their carbon dioxide footprint by adopting energy efficient green methods. In this paper, we aim to save energy by both switching base stations on/off and adaptively adjusting their transmission power according to the present traffic conditions for heterogenous wireless cellular networks. We formulate a novel linear programming model for the Traffic-Aware Topology Management (TAM) problem to find the best possible topology which minimizes the overall power consumption of the network while satisfying a certain Quality of Service level in Wideband Code Division Multiple Access packet-switched cellular networks. Although the optimization tools provide the optimum solutions, it is not possible to handle large instances due to the space and computational complexity. Hence, we propose a Green TAM Algorithm to solve the large-scale realistic instances of the formulated problem and compare our results with the results of two previously proposed methods, a greedy heuristic and a commercial optimization tool. We show that the proposed TAM scheme helps to maintain an energy-aware network and saves significant amount of energy by adjusting the network topology to the current traffic conditions adaptively.     

Visualization of graphical data in a user-specified 2D space using a weighted Isomap method

We present a visualization system for exploring the high-dimensional graphical data, such as textures or 3D models, in 2D space using the dimensionality reduction method. To arrange high-dimensional data in a meaningful 2D space, we develop a novel semi-supervised dimensionality reduction method that can embed data of high dimension in a user-defined 2D coordinate system that is meaningful in terms of the properties of the data. This is achieved by modifying the Isomap method by weighting the data so that the resulting coordinates have no degeneracies and are orthogonal.     

Finding representative landmarks of data on manifolds

Data-driven non-parametric models, such as manifold learning algorithms, are promising data analysis tools. However, to fit an off-training-set data point in a learned model, one must first “locate” the point in the training set. This query has a time cost proportional to the problem size, which limits the model's scalability. In this paper, we address the problem of selecting a subset of data points as the landmarks helping locate the novel points on the data manifolds. We propose a new category of landmarks defined with the following property: the way the landmarks represent the data in the ambient Euclidean space should resemble the way they represent the data on the manifold. Given the data points and the subset of landmarks, we provide procedures to test whether the proposed property presents for the choice of landmarks. If the data points are organized with a neighbourhood graph, as it is often conducted in practice, we interpret the proposed property in terms of the graph topology. We also discuss the extent to which the topology is preserved for landmark set passing our test procedure. Another contribution of this work is to develop an optimization based scheme to adjust an existing landmark set, which can improve the reliability for representing the manifold data. Experiments on the synthetic data and the natural data have been done. The results support the proposed properties and algorithms.     

Learning topology of a labeled data set with the supervised generative Gaussian graph

Extracting the topology of a set of a labeled data is expected to provide important information in order to analyze the data or to design a better decision system. In this work, we propose to extend the generative Gaussian graph to supervised learning in order to extract the topology of labeled data sets. The graph obtained learns the intra-class and inter-class connectedness and also the manifold-overlapping of the different classes. We propose a way to vizualize these topological features. We apply it to analyze the well-known Iris database and the three-phase pipe flow database.     

Design and configuration of neuro mechanical networks

Neuro mechanical network (NMN) is a new concept of adaptronic character. The governing idea is to include geometry, topology, load carrying, energy transfer, actuating, sensing and control of a machine in one single mathematical state model and, thereby, enable a formulation of the design and configuration problem as an optimization problem. We have focused our attention on a type of NMN consisting of what we call active trusses. For these, we have established a state model and given a design optimization problem from which we have obtained numerical solutions. These solutions show that the approach has the possibility to suggest new families of designs that are superior to those of classical passive trusses. We also indicate how activation may result in singularities, the treatment of which is, so far, essentially an open problem.     

Poly: A reliable and energy efficient topology control protocol for wireless sensor networks

Energy efficiency and reliability are the two important requirements for mission-critical wireless sensor networks. In the context of sensor topology control for routing and dissemination, Connected Dominating Set (CDS) based techniques proposed in prior literature provide the most promising efficiency and reliability. In a CDS-based topology control technique, a backbone - comprising a set of highly connected nodes - is formed which allows communication between any arbitrary pair of nodes in the network. In this paper, we show that formation of a polygon in the network provides a reliable and energy-efficient topology. Based on this observation, we propose Poly, a novel topology construction protocol based on the idea of polygons. We compare the performance of Poly with three prominent CDS-based topology construction protocols namely CDS-Rule K, Energy-efficient CDS (EECDS) and A3. Our simulation results demonstrate that Poly performs consistently better in terms of message overhead and other selected metrics. We also model the reliability of Poly and compare it with other CDS-based techniques to show that it achieves better connectivity under highly dynamic network topologies.     

基于局部重构与全局保持的半监督维数约减算法

针对基于局部与全局保持的半监督维数约减算法(LGSSDR)对部域参数选择比较敏感以及对部域图边权值设定不够准确的问题,提出一种基于局部重构与全局保持的半监督维数约减算法(工RGPSSDR)。该算法通过最小化局部重构误差来确定部域图的边权值,在保持数据集局部结构的同时能够保持其全局结构。在Extended YaleB和 CMU PIE标准人脸库上的实验结果表明LRGPSSDR算法的分类性能要优于其它半监督维数约减算法。     

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ad hoc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。     

Deployment of robust wireless sensor networks using gene regulatory networks: An isomorphism-based approach

The communication between nodes in a Wireless Sensor Network (WSN) may fail due to different factors, such as hardware malfunctions, energy depletion, temporal variations of the wireless channel and interference. To maximize efficiency, the sensor network deployment must be robust and resilient to such failures. One effective solution to this problem is to exploit a bio-inspired approach based on Gene Regulatory Networks (GRNs). Owing to million years of evolution, GRNs display intrinsic properties of adaptation and robustness, thus making them suitable for dynamic network environments. In this article, we exploit the genetic structure of real organisms to deploy bio-inspired WSNs that are isomorphic to certain GRN sub-networks. Exhaustive structural analysis, simulations and experimental results on a WSN testbed demonstrate that bio-inspired WSNs are resilient to node and link failures and offer better performance than existing solutions for robust WSNs.     

基于拓扑层级关系结构的便携式低压智能检测仪设计

针对传统智能检测仪灵活性差、灵敏度较低的问题,基于低压台区拓扑层级结构进行智能检测仪的设计,对低压台区拓扑关系进行星型拓扑改造,使低压台区结构更加简洁,数据完成互通;将智能检测仪管理模块进行集成化设计,使仪表检测数据控制力度更强;采用TMS320芯片加强检测仪数据处理速度,同时将检测仪功能模块集成化处理,缩小检测仪体积;利用多层级拓扑优化算法对检测仪采集数据分层次划分,并分析检测数据与时间周期的关系,从而完成条理化处理,最后通过测试网点对测量误差进行分析,发现该设计最大误差为0.9％,最小误差为0.2％;通过仿真实验发现该设计灵敏度最大,最高达到96％;通过对比实验发现该设计模型强控比率最大为0.8,控制时间为30 s;从而验证了该设计方法的优越性,证实了该研究的可行性.     

Online summarization of dynamic time series data

Managing large-scale time series databases has attracted significant attention in the database community recently. Related fundamental problems such as dimensionality reduction, transformation, pattern mining, and similarity search have been studied extensively. Although the time series data are dynamic by nature, as in data streams, current solutions to these fundamental problems have been mostly for the static time series databases. In this paper, we first propose a framework to online summary generation for large-scale and dynamic time series data, such as data streams. Then, we propose online transform-based summarization techniques over data streams that can be updated in constant time and space. We present both the exact and approximate versions of the proposed techniques and provide error bounds for the approximate case. One of our main contributions in this paper is the extensive performance analysis. Our experiments carefully evaluate the quality of the online summaries for point, range, and k–nn queries using real-life dynamic data sets of substantial size. Edited by W. Aref     

基于XML的案例表示和案例库构造方法

将基于案例推理(CBR)技术与XML结合,提出了基于XML的案例表示方法,给出了DTD定义,分析了它与传统数据库相比的优势,并以Snort规则的基于XML的案例化为例,证明了该方法的有效性.所做工作为CBR的研究提供了一些新的思路.