Impact of sources and destinations on the observed properties of the internet topology

Maps of the internet topology are generally obtained by measuring the routes from a given set of sources to a given set of destinations (with tools such as traceroute). It has been shown that this approach misses some links and nodes. Worse, in some cases it can induce a bias in the obtained data, i.e. the properties of the obtained maps are significantly different from those of the real topology. In order to reduce this bias, the general approach consists in increasing the number of sources. Some works have studied the relevance of this approach. Most of them have used theoretical results, or simulations on network models. Some papers have used real data obtained from actual measurement procedures to evaluate the importance of the number of sources and destinations, but no work to our knowledge has studied extensively the importance of the choice of sources or destinations. Here, we use real data from internet topology measurements to study this question: by comparing partial measurements to our complete data, we can evaluate the impact of adding sources or destinations on the observed properties.We show that the number of sources and destinations used plays a role in the observed properties, but that their choice, and not only their number, also has a strong influence on the observations. We then study common statistics used to describe the internet topology, and show that they behave differently: some can be trusted once the number of sources and destinations are not too small, while others are difficult to evaluate.     

Evaluation of a new method for measuring the internet degree distribution: Simulation results

Many contributions rely on the degree distribution of the Internet topology. However, current knowledge of this property is based on biased and erroneous measurements and is subject to much debate. Recently, in [1], a new approach, referred to as the Neighborhood Flooding method, was proposed to avoid issues raised by classical measurements. It aims at measuring the neighborhood of Internet core routers by sending traceroute probes from many monitors distributed in the Internet towards a given target router. In this paper, we investigate the accuracy of this method with simulations. Our results show that Neighborhood Flooding is free from the bias highlighted in the classical approach and is able to observe properly the exact degree of a vast majority of nodes in the core of the network. We show how the quality of the estimation depends on the number of monitors used and we carefully examine the influence of parameters of the simulations on our results. We also point out some limitations of the Neighborhood Flooding method and discuss their impact on the observed distribution.     

Palmtree: An IP alias resolution algorithm with linear probing complexity

Internet topology mapping studies utilize large scale topology maps to analyze various characteristics of the Internet. IP alias resolution, the task of mapping IP addresses to their corresponding routers, is an important task in building such topology maps. In this paper, we present a new probe-based IP alias resolution tool called palmtree. Palmtree can be used to complement the existing schemes in improving the overall success of alias resolution process during topology map construction. In addition, palmtree incurs a linear probing overhead to identify IP aliases. The experimental results obtained over Internet2 and GEANT networks as well as four major Internet Service Providers (ISPs) present quite promising results on the utility of palmtree in obtaining more accurate network topology maps.     

非广延熵:一种新的Internet拓扑度量

随着Internet规模的不断扩大,Internet拓扑模型已成为网络领域的研究热点。从最初的Waxman随机模型到基于幂律[1]特征提出的BA模型、GLP模型、PFP模型,都试图去发现和解析Internet拓扑演化的规律。节点度、聚簇系数、介数等拓扑度量分别被提出,用于刻画拓扑特征。引入统计物理学上非广延熵[2]作为一个新的拓扑度量,从概率分布的均匀程度上刻画拓扑模型的节点度分布的特征。为了定量地分析非广延熵在不同模型上的差异,通过从CAIDA获得最新的真实拓扑数据,比较BA模型和Waxman模型与真实拓扑数据在非广延熵上是否具有一致性,来验证非广延熵作为拓扑度量的合理性。最后指出BA模型的非广延熵接近真实的Internet拓扑,从而也说明非广延熵作为一种新的拓扑度量的可行性。     

基于网络编码的拓扑推断研究综述

拓扑结构是重要的网络性能参数之一,尤其是对网络监控和管理而言,获取网络拓扑是一个很有意义的基础问题。网络编码技术的出现为网络拓扑推断提供了新的思路,中间节点的网络编码操作使得编码数据与网络拓扑结构之间存在着紧密的联系,基于网络编码的拓扑推断正是利用这种联系对网络拓扑结构进行分析识别。与利用网络层析成像实现拓扑推断相比,基于网络编码的拓扑推断有许多优势,如提高拓扑推断的准确性、降低推断算法的复杂度等。总结了网络编码技术在网络拓扑推断中的应用及研究现状。     

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.     

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.     

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.     

Division-based rainfall-runoff simulations with BP neural networks and Xinanjiang model

The application of artificial neural network (ANN) to rainfall-runoff simulations has provided promising results in recent years. However, it is difficult to obtain satisfying results by using raw data for the direct prediction of the time series of streamflows. To improve simulating daily streamflow with back-propagation (BP) neural networks, the whole data set in this study is divided into two independent groups, flood period and non-flood period. The approaches and techniques of applying the division-based BP (DBP) in runoff simulation are presented in this paper. A comparison of the DBP model to the primitive BP model and the Xinanjiang model was also conducted to evaluate the effectiveness of the improvement. The numerical experimental results indicate that DBP model still overestimated flow peak, but improved considerably the streamflow simulation in the non-flood period.     

S-BITE: A Structure-Based Internet Topology gEnerator

The modeling and analysis of large-scale complex systems, such as the Internet, has recently become a hot research topic. We propose a Structure-Based Internet Topology gEnerator (S-BITE) aimed at accurately reproducing the Internet at the Autonomous System (AS) level. The proposed generator exploits a technique that partitions the network topology into two distinct blocks: the Core, which captures the underlying community structure of the Internet, and the Periphery, representing the “tendrils” of the topology. The benefits of this innovative technique are twofold. First, it deals with the high heterogeneity of the Internet by highlighting a small yet well-structured core. This leads to a huge reduction in complexity and shows that the core of the large-scale Internet is not that large, and can further be broken down into a two-layer graph. Second, thanks to the simplifications introduced by the topology layering, it leads us to the definition of a new topology generator, first at the core level and then for the whole Internet.To the best of our knowledge, S-BITE is the first generator that successfully targets the problem of both matching classical graph metrics, such as the degree distribution, and representing the Internets structure, in the form of maximal cliques. A comparison shows how S-BITE outperforms the reference generators in the literature when looking at both statistical and structural properties of the Internet.     

Computer simulations of exponentially convergent networks with large impulses

This paper demonstrates the use of a semi-discretization technique for obtaining a discrete-time analogue of an exponentially convergent network that is subject to impulses with large magnitude. Prior to implementing the analogue for computer simulations, we investigate its exponential convergence towards a unique equilibrium state and thereby obtain a family of sufficiency conditions governing the network parameters and the impulse magnitude and frequency. Although the time-step does not appear in the conditions that govern the network parameters, its value needs to be sufficiently small in order for the analogue displays correct convergence behaviour of the network when subjected particularly to large impulses.     

Exploring Unknown Environments with Obstacles

Abstract. We study exploration problems where a robot has to construct a complete map of an unknown environment using a path that is as short as possible. In the first problem setting we consider, a robot has to explore n rectangles. We show that no deterministic or randomized online algorithm can be better than Ω(sqrt n ) -competitive, solving an open problem by Deng et al. [7]. We also generalize this bound to the problem of exploring three-dimensional rectilinear polyhedra without obstacles. In the second problem setting we study, a robot has to explore a grid graph with obstacles in a piecemeal fashion. The piecemeal constraint was defined by Betke et al. [4] and implies that the robot has to return to a start node every so often. Betke et al. gave an efficient algorithm for exploring grids with rectangular obstacles. We present an efficient strategy for piecemeal exploration of grids with arbitrary rectilinear obstacles.     

Exploring pseudo- and chaotic random Monte Carlo simulations

Computer simulations are an increasingly important area of geoscience research and development. At the core of stochastic or Monte Carlo simulations are the random number sequences that are assumed to be distributed with specific characteristics. Computer-generated random numbers, uniformly distributed on (0, 1), can be very different depending on the selection of pseudo-random number (PRN) or chaotic random number (CRN) generators. In the evaluation of some definite integrals, the resulting error variances can even be of different orders of magnitude. Furthermore, practical techniques for variance reduction such as importance sampling and stratified sampling can be applied in most Monte Carlo simulations and significantly improve the results. A comparative analysis of these strategies has been carried out for computational applications in planar and spatial contexts. Based on these experiments, and on some practical examples of geodetic direct and inverse problems, conclusions and recommendations concerning their performance and general applicability are included.     

Exploring Linked Data with contextual tag clouds

In this paper we present the contextual tag cloud system: a novel application that helps users explore a large scale RDF dataset. Unlike folksonomy tags used in most traditional tag clouds, the tags in our system are ontological terms (classes and properties), and a user can construct a context with a set of tags that defines a subset of instances. Then in the contextual tag cloud, the font size of each tag depends on the number of instances that are associated with that tag and all tags in the context. Each contextual tag cloud serves as a summary of the distribution of relevant data, and by changing the context, the user can quickly gain an understanding of patterns in the data. Furthermore, the user can choose to include RDFS taxonomic and/or domain/range entailment in the calculations of tag sizes, thereby understanding the impact of semantics on the data. In this paper, we describe how the system can be used as a query building assistant, a data explorer for casual users, or a diagnosis tool for data providers. To resolve the key challenge of how to scale to Linked Data, we combine a scalable preprocessing approach with a specially-constructed inverted index, use three approaches to prune unnecessary counts for faster online computations, and design a paging and streaming interface. Together, these techniques enable a responsive system that in particular holds a dataset with more than 1.4 billion triples and over 380,000 tags. Via experimentation, we show how much our design choices benefit the responsiveness of our system.     

Exploring depression and problematic internet use among college females: A multisite study

ObjectiveThe purpose of this study was to assess associations between depression and problematic internet use (PIU) among female college students, and determine whether Internet use time moderates this relationship.MethodThis cross-sectional survey included 265 female college students from four U.S. universities. Students completed the Patient Health Questionnaire-9 (PHQ-9), the Problematic and Risky Internet Use Screening Scale (PRIUSS) and self-reported daily Internet use. Analyses included multivariate analysis of variance and Poisson regression.ResultsParticipants reported mean age of 20.2 years (SD = 1.7) and were 84.9% Caucasian. The mean PHQ-9 score was 5.4 (SD = 4.6); the mean PRIUSS score was 16.4 (SD = 11.1). Participants’ risk for PIU increased by 27% with each additional 30 min spent online using a computer (RR = 1.27, 95% CI: 1.14–1.42, p < .0001). Risk for PIU was significantly increased among those who met criteria for severe depression (RR = 8.16 95% CI: 4.27–15.6, p < .0001). The PHQ-9 items describing trouble concentrating, psychomotor dysregulation and suicidal ideation were most strongly associated with PIU risk.ConclusionsThe positive relationship between depression and PIU among female college students supports screening for both conditions, particularly among students reporting particular depression symptoms.     

Beyond adaptive security coping behaviors: Theory and empirical evidence

Extant research seldom focuses on maladaptive security coping behaviors. Applying the extended parallel process model, this study has developed a research model to reveal the processes underlying users’ adaptive and maladaptive security coping behaviors. The model is empirically examined along with alternative models. Results show that perceived coping efficacy is the most influencing factor promoting adaptive coping behaviors and deterring maladaptive coping behaviors. Fear plays a mediating role in the threat appraisal process and leads to adaptive and maladaptive coping behaviors. Trust in the Internet as the contextual factor influences the threat and coping appraisal processes and adaptive coping behaviors.     

Topology management techniques for tolerating node failures in wireless sensor networks: A survey

In wireless sensor networks (WSNs) nodes often operate unattended in a collaborative manner to perform some tasks. In many applications, the network is deployed in harsh environments such as battlefield where the nodes are susceptible to damage. In addition, nodes may fail due to energy depletion and breakdown in the onboard electronics. The failure of nodes may leave some areas uncovered and degrade the fidelity of the collected data. However, the most serious consequence is when the network gets partitioned into disjoint segments. Losing network connectivity has a very negative effect on the applications since it prevents data exchange and hinders coordination among some nodes. Therefore, restoring the overall network connectivity is very crucial. Given the resource-constrained setup, the recovery should impose the least overhead and performance impact. This paper focuses on network topology management techniques for tolerating/handling node failures in WSNs. Two broad categories based on reactive and proactive methods have been identified for classifying the existing techniques. Considering these categories, a thorough analysis and comparison of all the recent works have been provided. Finally, the paper is concluded by outlining open issues that warrant additional research.     

Architectures for the future networks and the next generation Internet: A survey

Networking research funding agencies in USA, Europe, Japan, and other countries are encouraging research on revolutionary networking architectures that may or may not be bound by the restrictions of the current TCP/IP based Internet. We present a comprehensive survey of such research projects and activities. The topics covered include various testbeds for experimentations for new architectures, new security mechanisms, content delivery mechanisms, management and control frameworks, service architectures, and routing mechanisms. Delay/disruption tolerant networks which allow communications even when complete end-to-end path is not available are also discussed.     

Unplugged: Exploring the costs and benefits of constant connection

Computer-mediated-communication (CMC) is a ubiquitous part of people’s lives, yet little research has investigated attitudes about unplugging, also known as media refusal. In this large-scale lifespan study we surveyed 446 participants ages 14–79 about their feelings and attitudes toward unplugging from CMC for 24 h. We also probed their actual recent experiences of unplugging. We were particularly interested in age differences. As predicted, younger people reported more negative feelings about unplugging, and people who reported higher scores on a loneliness survey also expressed more negative feelings. However, contrary to our hypothesis, there were no significant age differences in the length of time participants typically spent unplugged. Open-ended responses revealed that participants felt a mix of emotions about unplugging from CMC and were ambivalent about its use for connection; “connecting with family and friends” was listed as both a loss and a gain of unplugging. In addition, prior experience unplugging predicted less anxiety about a future anticipated unplugging experience. We discuss age-related themes that emerged about costs and benefits to unplugging, anticipated unplugging activities, reasons for unplugging, and the inherent complexity of retreating from the web of digital technology and its pull of readily available community, connection, and information.