Connectivity and coverage maintenance in wireless sensor networks

One of the main design challenges for wireless sensor networks (WSNs) is to obtain long system lifetime without sacrificing system original performance such as communication connectivity and sensing coverage. A large number of sensor nodes are deployed in redundant fashion in dense sensor networks, which lead to higher energy consumption. We propose a distributed framework for energy efficient connectivity and coverage maintenance in WSNs. In our framework, each sensor makes self-scheduling to separately control the states of RF and sensing unit based on dynamic coordinated reconstruction mechanism. A novel energy-balanced distributed connected dominating set algorithm is presented to make connectivity maintenance; and also a distributed node sensing scheduling is brought forward to maintain the network coverage according to the surveillance requirements. We implemented our framework by C++ programming, and the simulation results show that our framework outperforms several related work by considerably improving the energy performance of sensor networks to effectively extend network lifetime.     

基于fMRI动态功能连接的抑郁症患者分类研究*

针对当前抑郁症诊断正确率偏低、误诊率偏高的问题,利用fMRI动态功能连接研究了抑郁症辅助诊断问题。首先采用滑动时间窗技术研究功能连接及其网络拓扑特性的动态变化,然后基于这些动态特征应用多元模式分析方法对22名抑郁症患者和27名健康被试进行分类。采用动态分析方法能够增加样本数量,从而更加有利于一些分类算法的应用。实验结果表明以动态功能连接和网络拓扑特性为特征的分类正确率均为93.88%,明显优于对应非动态特征81.63%和85.71%的结果。进一步分析表明,具有高辨别力的特征所对应的脑区主要分布在默认网络、情感网络、视觉皮层区等,动态功能连接可能为抑郁症的辅助诊断提供新的手段。     

基于生物脑网络连接特性的高效神经网络算法

人工神经网络发展至今,已经在计算机视觉、类脑智能等方面得到广泛应用.在过去几十年中,人们对神经网络的研究注重追求更高的准确率,从而忽略了对网络计算成本的控制.而人脑作为高效且节能的网络,其对人工智能的发展起到了重要启示作用.如何仿真生物脑网络的连接特性,建立超低能耗的人工神经网络模型实现基本相同的目标识别正确率成为当前研究的热点.为建立低能耗的人工神经网络模型,本文结合大脑网络的连接特性,通过改变人工神经网络的连接实现网络的高效性.实验结果表明,结合生物脑网络的连接特性,改变网络的连接,很大程度上减少了网络的计算成本,而网络的性能并没有受到明显影响.     

Lower Bounds for Fully Dynamic Connectivity Problems in Graphs

We prove lower bounds on the complexity of maintaining fully dynamic k -edge or k -vertex connectivity in plane graphs and in (k-1) -vertex connected graphs. We show an amortized lower bound of (log n / {k (log log n} + log b)) per edge insertion, deletion, or query operation in the cell probe model, where b is the word size of the machine and n is the number of vertices in G . We also show an amortized lower bound of (log n /(log log n + log b)) per operation for fully dynamic planarity testing in embedded graphs. These are the first lower bounds for fully dynamic connectivity problems. Received January 1995; revised February 1997.     

The Architecture of Connectivity: A Key to Network Vulnerability,Complexity and Resilience

This paper highlights the relevance of connectivity and its architecture as a general conceptual framework which underlies and integrates the concepts of network vulnerability, complexity, and resilience. In particular, it will be pointed out that connectivity architecture can be considered an explicit key element for network vulnerability and shock propagation. While the relevance of the various connectivity configurations is not clearly emphasised in the dynamic complexity models of the space-economy, it appears to play a primary role in network analysis. In this regard, the emerging recognition of connectivity architecture in relation to hubs ? and hierarchies of hubs ? in a complex network will help the enhancement of network resilience. The paper develops as follows. First, the notion of network vulnerability, which refers not only to the phenomenon of shocks, but also to the propagation of shocks in a network, will be examined. Here it appears that modelling vulnerability and shock propagation, also jointly with cascading disaster models, is strongly based on connectivity issues. The question is: How can conventional (complex) system dynamic modelling, as well as network modelling, take into account these shocks and connectivity dynamics from the methodological viewpoint? A review in this respect shows how connectivity is a ‘hidden’ element in these complexity models, for example, in chaos or (dynamic) competition models, where interaction parameter values might lead to vulnerable domains and chaotic behaviour. On the contrary, connectivity and its various topologies have a distinct, primary role in network analysis. The issue of network resilience appears therefore to be the ‘response’ to vulnerability and chaos, calling for robustness and stability of the network in the presence of shocks and disruptions. Resilience analysis refers to the speed at which a network returns to its equilibrium after a shock, as well as to the perturbations/shocks that can be absorbed before the network is induced into some other equilibrium (adaptivity). Connectivity is relevant here, but not often considered in spatial economics. In order to reach a unified methodological framework, attention will finally be paid to a complementary analysis of the (dynamic) concepts of vulnerability and resilience. In this light, chaos models/properties might be seen in a positive perspective, since small changes can lead to uncertain and unstable effects, but also, thanks to connectivity, to new equilibria which are not necessarily negative. Thus, the architecture of connectivity, in its interdisciplinary insights, can be considered as a fundamental (and analytical) approach for identifying vulnerability and resilience patterns in complex networks.

     

fMRI动态功能网络构建及其在脑部疾病识别中的应用

如何从复杂的fMRI数据中提取 丰富的大脑信息是提高脑部疾病识别精度的关键。传统的静息态功能磁共振成像分析中,功能连接网络被认为是稳定不变的。提出一种基于成组独立成分分析的构建动态功能连接网络的方法,并通过该网络来获取功能网络本身的动态特性。首先,利用成组独立成分分析法提取fMRI数据的空间独立成分作为网络节点,并通过滑动时间窗的方法获取窗口时间序列,构建动态功能连接网络。以动态功能网络作为特征,对精神分裂症患者和正常被试数据进行分类识别。实验结果表明,该方法能够获取fMRI数据的时间维度信息,提高识别效果,在一定程度上能为临床诊断提供客观参照。     

Connectivity recovery in post-disaster scenarios through Cognitive Radio swarms

In the aftermath of a natural calamity, relief operations can be hindered by damages to the terrestrial infrastructures (e.g. cellular base stations) that might lead to the disruption of wireless communication services. As a result, network partitions made up of isolated End-User (EU) devices, heterogeneous in terms of wireless access technologies and transmitting frequency bands, can occur within the scenario. In this paper, we address the problem of how to deploy a temporary and dynamic wireless network in order to quickly re-establish the end-to-end connectivity among isolated devices in a post-disaster environment. To this purpose, we propose the utilization of Repairing Units (RUs), consisting of Unmanned Ground Vehicles (UGVs) equipped with multiple Cognitive Radio (CR) devices; swarms of RUs are able to self-organize into a Repairing Mesh Network (RMN) that connects the isolated EU devices. Three main contributions are provided in this paper. First, we address the theoretical problem of determining the optimal deployment of the RMN (in terms of position and channel allocation on each RU), so that the number of connected EU devices is maximized, given a constrained number of available RUs. We further divide the deployment problem into a multi-channel spatial coverage and mesh connectivity problems, and we provide an approximated (optimal) solution. Second, we propose a distributed algorithm—based on the virtual spring force model—through which the RUs are able to explore the scenario in terms of space/frequency, and to create the RMN. Third, we evaluate connectivity and adaptiveness of the distributed solution through extensive Omnet++ simulations and a small scale test-bed. Simulation results show that the distributed RMN deployment algorithm provides performance close to the approximated solution in terms of covered EU devices. Experimental results demonstrate the ability of the distributed virtual spring model to adapt to dynamic propagation conditions, in order to maximize the quality of the wireless links of the RMN.     

衰落信道下车载自组织网络连通性分析

以孤立节点数、最大连通长度、全局网络效率等参数为连通性量化指标,应用VanetMobisim车辆仿真软件建立车载自组织网络,详细研究在Nakagami衰落信道模型和确定信道模型下VANETs连通性随时间的演化特征。结果表明当VANETs全连通时,网络时间相关性较弱,当网络连通性比较差时,各项连通性指标均呈现重尾现象,网络连通性具有很强的时间相关性。分析车辆节点密度、路径损耗指数和衰落因子等因素对网络连通性的影响。     

Connectivity and Inference Problems for Temporal Networks

Many network problems are based on fundamental relationships involving time. Consider, for example, the problems of modeling the flow of information through a distributed network, studying the spread of a disease through a population, or analyzing the reachability properties of an airline timetable. In such settings, a natural model is that of a graph in which each edge is annotated with a time label specifying the time at which its endpoints “communicated.” We will call such a graph a temporal network. To model the notion that information in such a network “flows” only on paths whose labels respect the ordering of time, we call a path time-respecting if the time labels on its edges are non-decreasing. The central motivation for our work is the following question: how do the basic combinatorial and algorithmic properties of graphs change when we impose this additional temporal condition? The notion of a path is intrinsic to many of the most fundamental algorithmic problems on graphs; spanning trees, connectivity, flows, and cuts are some examples. When we focus on time-respecting paths in place of arbitrary paths, many of these problems acquire a character that is different from the traditional setting, but very rich in its own right. We provide results on two types of problems for temporal networks. First, we consider connectivity problems, in which we seek disjoint time-respecting paths between pairs of nodes. The natural analogue of Menger's Theorem for node-disjoint paths fails in general for time-respecting paths; we give a non-trivial characterization of those graphs for which the theorem does hold in terms of an excluded subdivision theorem, and provide a polynomial-time algorithm for connectivity on this class of graphs. (The problem on general graphs is NP-complete.) We then define and study the class of inference problems, in which we seek to reconstruct a partially specified time labeling of a network in a manner consistent with an observed history of information flow.     

Solving an algebraic path problem and some related graph problemson a hyper-bus broadcast network

The parallel computation model upon which the proposed algorithms are based is the hyper-bus broadcast network. The hyper-bus broadcast network consists of processors which are connected by global buses only. Based on such an improved architecture, we first design two O(1) time basic operations for finding the maximum and minimum of N numbers each of size O(log N)-bit and computing the matrix multiplication operation of two N×N matrices, respectively. Then, based on these two basic operations, three of the most important instances in the algebraic path problem, the connectivity problem, and several related problems are all solved in O(log N) time. These include the all-pair shortest paths, the minimum-weight spanning tree, the transitive closure, the connected component, the biconnected component, the articulation point, and the bridge problems, either in an undirected or a directed graph, respectively     

二进制递归网络的连通性

二进制递归网络是超立方体的一类特殊变体,它具有很多良好的网络特性。网络的连通性是衡量网络结构通信能力的一个重要性能,虽然到目前为止已知的一些二进制递归网 络的连通性都已被研究过,但这些研究只是针对个体进行的,并不能代表所有二进制递归网络的连通特性。本文通过证明任何一个二进制递归网络中的每对顶点之间只能存在在”条顶点不交路,得到了整个二进制递归网络的点和边连通度皆为”的重要结论。     

Connectivity and performance tradeoffs in the cascade correlationlearning architecture

The cascade correlation is a very flexible, efficient and fast algorithm for supervised learning. It incrementally builds the network by adding hidden units one at a time, until the desired input/output mapping is achieved. It connects all the previously installed units to the new unit being added. Consequently, each new unit in effect adds a new layer and the fan-in of the hidden and output units keeps on increasing as more units get added. The resulting structure could be hard to implement in VLSI, because the connections are irregular and the fan-in is unbounded. Moreover, the depth or the propagation delay through the resulting network is directly proportional to the number of units and can be excessive. We have modified the algorithm to generate networks with restricted fan-in and small depth (propagation delay) by controlling the connectivity. Our results reveal that there is a tradeoff between connectivity and other performance attributes like depth, total number of independent parameters, and learning time.     

基于功能磁共振成像的人脑效应连接网络识别方法综述

人脑效应连接网络刻画了脑区间神经活动的因果效应. 对不同人群的脑效应连接网络进行研究不仅能为神经精神疾病病理机制的理解提供新视角, 而且能为疾病的早期诊断和治疗评价提供新的脑网络影像学标记, 具有十分重要的理论意义和应用价值. 利用计算方法从功能磁共振成像(Functional magnetic resonance imaging, fMRI)数据中识别脑效应连接网络是目前人脑连接组学中一项重要的研究课题. 本文首先概括了从fMRI数据中进行脑效应连接网络识别的主要流程, 说明了其中的主要步骤和方法; 然后, 给出了一种脑效应连接网络识别方法的分类体系, 并对其中一些代表性的识别算法进行了阐述; 最后, 通过对该领域挑战性问题的分析, 预测了脑效应连接网络识别未来的研究方向, 以期对相关研究提供一定的参考.     

融合共空间模式与脑网络特征的EEG抑郁识别

提出共空间模式算法和脑网络拓扑属性融合的脑电信号（electroencephalography,EEG）特征,结合深度学习模型时序卷积网络（temporal convolution network,TCN）对抑郁组和对照组进行分类。根据相位锁值构建电极通道间相位同步性功能网络,分析不同频段下两种类别的功能连接模式。采用多特征融合方法将共空间模式特征和脑网络拓扑特征结合起来,最后结合Fisher score特征选择方法和分类器依赖结构,得到低维高效的特征子集并应用TCN进行分类。在抑郁数据集上的实验结果验证了所提策略的有效性。     

无线传感器网络拓扑连通性与稀疏性研究

连通性与稀疏性是无线传感器网络的重要拓扑属性,针对良好的网络拓扑既要保证连通又要适当降低连边密度的问题,首先分析了网络连通概率的相变特性,发现存在临界传输半径,在此临界值周围网络连通概率会发生0-1相变.其次,在网络以较小的传输半径保持连通的情况下,以度和介数作为衡量节点重要性的指标,提出了稀疏网络拓扑优化算法,通过适...     

基于代数连通度的域内节能路由算法

通过节能路由算法减少网络能耗是网络中需要解决的一个关键性的科学问题。如今已有的节能方案都是在已知流量矩阵的前提下研究网络节能,由于实时流量难以获取,使得这些方案都难以在实际中部署。因此,文中提出一种基于代数连通度的域内节能方案(Intra-domain Energy Efficient Routing Scheme Based on Algebraic Connectivity,EERSBAC)。EERSBAC不需要网络中的实时流量矩阵,仅依靠网络中的拓扑结构就可以实现节能。首先,提出链路关键度模型,利用链路关键度模型计算出网络中所有链路的重要程度;然后,提出代数连通度模型,利用代数连通度模型可以定量的衡量网络的连通性能。实验结果表明,EERSBAC不仅能够降低网络能耗,而且具有较小的路径拉伸度。     

Restoration by path concatenation: fast recovery of MPLS paths

A new general theory about restoration of network paths is first introduced. The theory pertains to restoration of shortest paths in a network following failure, e.g., we prove that a shortest path in a network after removing k edges is the concatenation of at most k+1 shortest paths in the original network. The theory is then combined with efficient path concatenation techniques in MPLS (multi-protocol label switching), to achieve powerful schemes for restoration in MPLS based networks. We thus transform MPLS into a flexible and robust method for forwarding packets in a network. Finally, the different schemes suggested are evaluated experimentally on three large networks (a large ISP, the AS graph of the Internet, and the full Internet topology). These experiments demonstrate that the restoration schemes perform well in actual topologies. Received: December 2001 / Accepted: July 2002 RID="*" ID="*" This research was supported by a grant from the Ministry of Science, Israel     

无线传感器网络三维表面k覆盖多连通部署方法*

无线传感器网络中覆盖连通问题是基本且重要的问题,三维表面作为无线传感器网络中的一种特殊情形,对应于现实世界中的山体,为了解决这类与实际应用密切相关的问题,提出了三维表面k覆盖多连通部署方法。该方法结合三维表面的地形特征,首先在目标区域自由选择网格大小进行划分,接着在各网格之间建立多连通关系,再通过方向梯度概率感知模型在网格内先构造k覆盖集,然后利用最小生成树算法构造连通图,最后找出关节点构造双连通图。大量仿真实验表明,该方法能够对目标区域进行完全覆盖和连通,并且能保证网络的健壮性。     

受限波兹曼机联合稀疏近似的脑功能检测模型

人脑功能连通性检测是神经科学研究的重要技术．使用受限制波兹曼机（RestrictedBoltzmannMachine,RBM）对大量多被试功能磁共振（functionalMagneticResonanceImaging,fMRI）数据进行建模可以检测人脑功能连接,但是不能有效检测单被试数据的功能连接．本文研究一种新颖的融合了稀疏近似与RBM技术的脑功能连通性检测模型,该模型充分利用fMRI数据的稀疏性,采用稀疏近似理论对fMRI数据进行空间域稀疏近似压缩,然后使用RBM建立模型,以检测脑功能连通性．实验结果表明,该融合模型可以有效地提取单被试数据的脑功能时间域混合模型及其相应的脑功能图谱,解决了RBM在单被试数据分析上的瓶颈．     

嵌入式网络连通检测器的实现

介绍了以太网连通错误管理（CFM）的技术背景,指出纯软件开发的以太网的CFM模块具有局限性,并无法满足协议多变的特点。该模块是在SOPC系统上实现IEEE802．1ag协议中连通性检测功能。系统采用Altera公司提供的DE2开发板作为实现平台,实现了能以毫秒级速度发送及分级告警的嵌入式网络连通检测器。用两个开发板模拟一个小型的网络,通过PC机来对开发板进行操作和控制,利用对开发板上的LCD显示屏及LED显示灯进行监测,验证连通检测器的功能及性能。