自组织网络演化中的连通性分析

自组织网络的连通性是保证网络通信能力的根本。针对无中心的自组织网络,分析自组织演化行为过程中网络的连通性,重点阐述了真实网络拥有的局域特征以及自组织网络节点的择优邻居选择策略,对于网络连通性能的影响。经过理论分析和仿真模拟发现,在类Gossip机制的自组织模式下,网络的连通性被破坏的概率很大。     

Connectivity reliability in uncertain networks with stability analysis

This paper treats the fundamental problems of reliability and stability analysis in uncertain networks. Here, we consider a collapsed, post-disaster, traffic network that is composed of nodes (centers) and arcs (links), where the uncertain operationality or reliability of links is evaluated by domain experts. To ensure the arrival of relief materials and rescue vehicles to the disaster areas in time, uncertainty theory, which neither requires any probability distribution nor fuzzy membership function, is employed to originally propose the problem of choosing the most reliable path (MRP). We then introduce the new problems of α-most reliable path (α-MRP), which aims to minimize the pessimistic risk value of a path under a given confidence level α, and very most reliable path (VMRP), where the objective is to maximize the confidence level of a path under a given threshold of pessimistic risk. Then, exploiting these concepts, we give the uncertainty distribution of the MRP in an uncertain traffic network. The objective of both α-MRP and VMRP is to determine a path that comprises the least risky route for transportation from a designated source node to a designated sink node, but with different decision criteria. Furthermore, a methodology is proposed to tackle the stability analysis issue in the framework of uncertainty programming; specifically, we show how to compute the arcs’ tolerances. Finally, we provide illustrative examples that show how our approaches work in realistic situation.     

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。     

一种中转换乘航线网实用化建模方法研究

航空旅行的某些独到优势是铁路、公路与水运所不具备的,但如何最大限度地将其发挥出来往往却异常艰难。结合民航建模基本特性和面临难题剖析,比较系统、全面地论述了不同出行方式的模型联接、地域广阔性与信息庞杂性消解、时变与非精确表示,以及机器学习等的求解策略,同时给出了实验系统建造的若干技术细节解决思路。     

Connectivity in finite ad-hoc networks

Research on ad-hoc network connectivity has mainly focused on asymptotic results in the number of nodes in the network. For a one-dimensional ad-hoc network G1, assuming all the nodes are independently uniform distributed in a closed interval [0, Z]（z ∈ R^＋）, we derive a generic formula for the probability that the network is connected. The finite connected ad-hoc networks is analyzed. And we separately suggest necessary conditions to make the ad-hoc network to be connected in one and two dimensional cases, facing possible failed nodes （f-nodes）. Based on the necessary condition and unit-disk assumption for the node transmission, we prove that the nodes of the connected two-dimensional ad-hoc networks （G2） can be divided into at most five different groups. For an f-node no in either of the five groups, we derive a close formula for the probability that there is at least one route between a pair of nodes in G2 -- {no}.     

Single-exception sorting networks and the computational complexity of optimal sorting network verification

Asorting network is a combinational circuit for sorting constructed from comparison-swap units. The depth of such a circuit is a measure of its running time. It is known that sorting-network verification is computationally intractable. However, it is reasonable to hypothesize that only the fastest (that is, the shallowest) networks are likely to be fabricated. It is shown that the verification of shallow sorting networks is also computationally intractable. Firstly, a method for constructing asymptotically optimalsingle-exception sorting networks is demonstrated. These are networks which sort all zero-one inputs except one. More specifically, their depth isD(n-1)+2log(n-1)+2, whereD(n) is the minimum depth of ann-input sorting network. It follows that the verification problem for sorting networks of depth 2D(n)+6logn+O(1) is Co-NP complete. Given the current state of knowledge aboutD(n) for largen, this indicates that the complexity of verification for shallow sorting networks is as great as for deep networks.This research was supported by NSF Grant CCR-8801659.     

Approval network: a novel approach for sentiment analysis in social networks

The data-centric impetus and the development of online social networks has led to a significant amount of research that is nowadays more flexible in demonstrating several sociological hypotheses, such as the sentiment influence and transfer among users. Most of the works regarding sentiment classification usually consider text as unique source of information, do not taking into account that social networks are actually networked environments. To overcome this limitation, two main sociological theories should be accounted for addressing any sentiment analysis tasks: homophily and constructuralism. In this paper, we propose Approval Network as a novel graph representation to jointly model homophily and constructuralism, which is intended to better represent the contagion on social networks. To show the potentiality of the proposed representation, two novel sentiment analysis models have been proposed. The first one, related to user-level polarity classification, is approached by presenting a semi-supervised framework grounded on a Markov-based probabilistic model. The second task, aimed at simultaneously extracting aspects and sentiment at message level, is addressed by proposing a novel fully unsupervised generative model. The experimental results show that the proposes sentiment analysis models grounded on Approval Networks are able to outperform not only the traditional models where the relationships are disregarded, but also those computational approaches based on traditional friendship connections.     

Connectivity monitoring in wireless sensor networks

Connectivity monitoring is useful in practical deployment of wireless sensor network. In order to understand the behavior and performance bottleneck, knowledge of the network connectivity is crucial. In this paper, we propose a flexible and efficient connectivity monitoring algorithm (H²CM) that has three components and operates in a divide-and-conquer manner. The components include hop vector distance based filtering, Bloom filters and signature hashing and are designed to work with different combinations of network and neighbor set sizes. In simulation, communication cost reduction of H²CM compare to maximal compression of neighborhood information varies from 65% to 85% for large networks (>1000 nodes) and from 40% to 70% for a medium size network (a few hundred nodes). We have also implemented the algorithm in TinyOS and evaluated its performance on a testbed with 34 motes. Lastly, we study the problem of node failure detection — a simple application of connectivity monitoring. We show that by combining H²CM with the concept of dominating set, the communication cost can be drastically reduced compare to traditional data collection method.     

Modeling and complexity in neural networks

In this paper, we study nonlinear spatio-temporal dynamics in synchronous and asynchronous chaotic neural networks from the viewpoint of the modeling and complexity of the dynamic brain. First, the possible roles and functions of spatio-temporal neurochaos are considered with a model of synchronous chaotic neural networks composed of a neuron model with a chaotic map. Second, deterministic point-process dynamics with spikes of action potentials is demonstrated with a biologically more plausible model of asynchronous chaotic neural networks. Last, the possibilities of inventing a new brain-type of computing system are discussed on the basis of these models of chaotic neural networks. This work was presented, in part, at the Third International Symposium on Artificial Life and Robotics, Oita, Japan, January 19–21, 1998.     

超立方体网络连通性的概率分析

超立方体网络是大型多处理器并行计算机系统中极为重要的拓扑结构.本文使用概率分析的方法研究了在给定结点错误概率的情况下,具有子连通性的超立方体网络容错模型的连通性。理论分析和试验结果表明:在具有大量分布结点错误情况下,超立方体网络是子连通的概率非常高。     

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.     

General-purpose computation with neural networks: a survey of complexity theoretic results

We survey and summarize the literature on the computational aspects of neural network models by presenting a detailed taxonomy of the various models according to their complexity theoretic characteristics. The criteria of classification include the architecture of the network (feedforward versus recurrent), time model (discrete versus continuous), state type (binary versus analog), weight constraints (symmetric versus asymmetric), network size (finite nets versus infinite families), and computation type (deterministic versus probabilistic), among others. The underlying results concerning the computational power and complexity issues of perceptron, radial basis function, winner-take-all, and spiking neural networks are briefly surveyed, with pointers to the relevant literature. In our survey, we focus mainly on the digital computation whose inputs and outputs are binary in nature, although their values are quite often encoded as analog neuron states. We omit the important learning issues.     

Connectivity opportunity selection in heterogeneous wireless multi-hop networks

This paper addresses the problem of selecting the best connectivity alternative for the user in a generic Heterogeneous Wireless Multi-hop Network (HWMN), integrating distinct wireless technologies and multi-mode cooperating stations. We propose a Connectivity opportunity Selection Algorithm (CSA) that uses network state information and mobility profile information to select the best connectivity based on the applications’ requirements. We provide a simulation-based performance evaluation of the CSA and compare it with a greedy network selection scheme. Furthermore, we propose an extended reference model that allows the integration of the concept of connectivity opportunity and our proposed CSA with the framework being defined by the upcoming IEEE 802.21 standard for Media Independent Handover services.     

Connectivity models of interdependency in mixed-type critical infrastructure networks

Determining interdependencies and cascading failure modes in critical infrastructures is a complex problem that is exacerbated further by the diverging characteristics of the interconnected infrastructure types. Services in some types of infrastructure such as telecommunications or the electric grid are provided and consumed instantly. Others, notably oil and gas but also other infrastructures built on physical resources, however, exhibit buffering characteristics. In this paper we describe a model for the abstract representation of both types of infrastructure networks and their interdependencies. The model is then validated and demonstrated using characteristic topologies and interconnections.     

The network complexity and the Turing machine complexity of finite functions

Summary Let L(f) be the network complexity of a Boolean function L(f). For any n-ary Boolean function L(f) let . Hereby p ranges over all relative Turing programs and ranges over all oracles such that given the oracle , the restriction of p to inputs of length n is a program for L(f). p is the number of instructions of p. T _p (n) is the time bound and S _p of the program p relative to the oracle on inputs of length n. Our main results are (1) L(f) O(TC(L(f))), (2) TC(f) O(L(f) ^{2 2+}) for every O.The results of this paper have been reported in a main lecture at the 1975 annual meeting of GAMM, April 2–5, Göttingen     

Reducing complexity in tree-like computer interconnection networks

Interconnection networks based on the k-ary n-tree topology are widely used in high-performance parallel computers. However, this topology is expensive and complex to build. In this paper we evaluate an alternative tree-like topology that is cheaper in terms of cost and complexity because it uses fewer switches and links. This alternative topology leaves unused upward ports on switches, which can be rearranged to be used as downward ports. The increase of locality might be efficiently exploited by applications. We test the performance of these thin-trees, and compare it with that of regular trees. Evaluation is carried out using a collection of synthetic traffic patterns that emulate the behavior of scientific applications and functions within message passing libraries, not only in terms of sources and destinations of messages, but also considering the causal relationships among them. We also propose a methodology to perform cost and performance analysis of different networks. Our main conclusion is that, for the set of studied workloads, the performance drop in thin-trees is less noticeable than the cost savings.     

Algebraic analysis of the topological properties of a banyan network and its application in fault-tolerant switching networks

In this paper, we introduce abstract algebraic analysis of the topological structure of a banyan network, which has become the baseline for most switching networks. The analysis provides the following key results: (1) The switching elements of a switching stage are arranged in order, that is, each stage of a banyan network consists of a series of a cyclic group. (2) The links between switching stages implement a homomorphism relationship in terms of self-routing. Therefore, we can recover the misrouting of a detour fault link by providing adaptive self-routing. (3) The cyclic group of a stage is a subgroup of that of the next stage, so that every stage and its adjacent stage make up a factor group. Based on this analysis, we introduce a cyclic banyan network that is more reliable than other switching networks. We present mathematical analysis of the reliability of the switching network to allow quantitative comparison against other switching networks.     

时序网络演化速度对传播的影响分析

为分析时序网络演化速度对传播过程的影响,通过改进已有的时序相关系数定义,给出了一个网络演化速度指标;同时,提出了一个具有非马尔可夫性质的时序网络演化模型。在每个时间步,每一个给定的激活节点都以概率r在网络中随机选择一个节点,以概率1-r在该激活节点的原邻居中随机选择一个节点,并在该激活节点与所选节点间建立连边。模拟结果表明：网络模型参数r与网络演化速度指标之间有单调增的关系;同时,激活节点随机连边的概率r越大,网络传播范围就越广。由此可知:演化速度快的时序网络有利于网络传播;进一步地,网络拓扑结构的快速变化有利于信息的快速传播,但不利于抑制病毒传播。     

密集WiFi网络环境网络分配矢量优化与性能分析

IEEE 802.11协议利用RTS/CTS帧交换来设置设备的网络分配矢量(NAV)。现有NAV方案未考虑密集部署场景,可能存在误清除的情况。提出一种简便可行的可计数NAV(C-NAV)设置方案,通过统计NAV设置信息的个数并进行实时更新以防止误清除,从而更有效地利用传输机会。对所提C-NAV方案进行了理论分析和仿真验证,证明在密集部署WiFi场景中,该方案能有效避免现有设置方案NAV时长浪费和错误清除等问题,提升网络的整体吞吐量。     

Learning capacity and sample complexity on expert networks

A major development in knowledge-based neural networks is the integration of symbolic expert rule-based knowledge into neural networks, resulting in so-called rule-based neural (or connectionist) networks. An expert network here refers to a particular construct in which the uncertainty management model of symbolic expert systems is mapped into the activation function of the neural network. This paper addresses a yet-to-be-answered question: Why can expert networks generalize more effectively from a finite number of training instances than multilayered perceptrons? It formally shows that expert networks reduce generalization dimensionality and require relatively small sample sizes for correct generalization.