3,128 research outputs found

    An Efficient and Reliable Data Transmission Service using Network Coding Algorithms in Peer-to-Peer Network

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    Network coding is a progressive enhancement in natural network routing that increases throughput and reliability for unicast, multicast, and even broadcast transmissions. P2P networks are ideal for implementing network coding algorithms for two reasons: I. A P2P network's topology isn't predetermined. As a result, designing a topology that is compatible with the network coding algorithm is much easier. II. Every peer is an end host in this network.  As a result, instead of saving and sending the message, complex network coding operations, such as encoding and decoding, are now easier to perform. The objective of this work is to use the best features of network coding algorithms and properly apply them to P2P networks to create an efficient and reliable data transmission service. The goal of the network coding algorithm is to make better use of network resources and thus increase P2P network capacity. An encoding algorithm that enables an intermediate peer to produce output messages by encoding (that is, computing a function of the data it receives. The decoder's role is to obtain enough encoded packets so that the original information can be recovered. This research work has measured an amount of hypothetical and applied consequences in which the network coding procedure or a variation of it is used to improve performance parameters such as throughput and reliability in P2P network data transmission based on network coding. The comparison of data transmission between network routing and network coding algorithms was the main focus of this paper.  According to our simulations, the new network coding systems can reach 15% to 20% upper throughput than supplementary P2P network routing systems

    Probabilistic methods for distributed information dissemination

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    Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2013.Cataloged from PDF version of thesis.Includes bibliographical references (p. 457-484).The ever-increasing growth of modern networks comes with a paradigm shift in network operation. Networks can no longer be abstracted as deterministic, centrally controlled systems with static topologies but need to be understood as highly distributed, dynamic systems with inherent unreliabilities. This makes many communication, coordination and computation tasks challenging and in many scenarios communication becomes a crucial bottleneck. In this thesis, we develop new algorithms and techniques to address these challenges. In particular we concentrate on broadcast and information dissemination tasks and introduce novel ideas on how randomization can lead to powerful, simple and practical communication primitives suitable for these modern networks. In this endeavor we combine and further develop tools from different disciplines trying to simultaneously addresses the distributed, information theoretic and algorithmic aspects of network communication. The two main probabilistic techniques developed to disseminate information in a network are gossip and random linear network coding. Gossip is an alternative to classical flooding approaches: Instead of nodes repeatedly forwarding information to all their neighbors, gossiping nodes forward information only to a small number of (random) neighbors. We show that, when done right, gossip disperses information almost as quickly as flooding, albeit with a drastically reduced communication overhead. Random linear network coding (RLNC) applies when a large amount of information or many messages are to be disseminated. Instead of routing messages through intermediate nodes, that is, following a classical store-and-forward approach, RLNC mixes messages together by forwarding random linear combinations of messages. The simplicity and topology-obliviousness of this approach makes RLNC particularly interesting for the distributed settings considered in this thesis. Unfortunately the performance of RLNC was not well understood even for the simplest such settings. We introduce a simple yet powerful analysis technique that allows us to prove optimal performance guarantees for all settings considered in the literature and many more that were not analyzable so far. Specifically, we give many new results for RLNC gossip algorithms, RLNC algorithms for dynamic networks, and RLNC with correlated data. We also provide a novel highly efficient distributed implementation of RLNC that achieves these performance guarantees while buffering only a minimal amount of information at intermediate nodes. We then apply our techniques to improve communication primitives in multi-hop radio networks. While radio networks inherently support broadcast communications, e.g., from one node to all surrounding nodes, interference of simultaneous transmissions makes multihop broadcast communication an interesting challenge. We show that, again, randomization holds the key for obtaining simple, efficient and distributed information dissemination protocols. In particular, using random back-off strategies to coordinate access to the shared medium leads to optimal gossip-like communications and applying RLNC achieves the first throughput-optimal multi-message communication primitives. Lastly we apply our probabilistic approach for analyzing simple, distributed propagation protocols in a broader context by studying algorithms for the Lovász Local Lemma. These algorithms find solutions to certain local constraint satisfaction problems by randomly fixing and propagating violations locally. Our two main results show that, firstly, there are also efficient deterministic propagation strategies achieving the same and, secondly, using the random fixing strategy has the advantage of producing not just an arbitrary solution but an approximately uniformly random one. Both results lead to simple, constructions for a many locally consistent structures of interest that were not known to be efficiently constructable before.by Bernhard Haeupler.Ph.D

    Towards efficacy and efficiency in sparse delay tolerant networks

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    The ubiquitous adoption of portable smart devices has enabled a new way of communication via Delay Tolerant Networks (DTNs), whereby messages are routed by the personal devices carried by ever-moving people. Although a DTN is a type of Mobile Ad Hoc Network (MANET), traditional MANET solutions are ill-equipped to accommodate message delivery in DTNs due to the dynamic and unpredictable nature of people\u27s movements and their spatio-temporal sparsity. More so, such DTNs are susceptible to catastrophic congestion and are inherently chaotic and arduous. This manuscript proposes approaches to handle message delivery in notably sparse DTNs. First, the ChitChat system [69] employs the social interests of individuals participating in a DTN to accurately model multi-hop relationships and to make opportunistic routing decisions for interest-annotated messages. Second, the ChitChat system is hybridized [70] to consider both social context and geographic information for learning the social semantics of locations so as to identify worthwhile routing opportunities to destinations and areas of interest. Network density analyses of five real-world datasets is conducted to identify sparse datasets on which to conduct simulations, finding that commonly-used datasets in past DTN research are notably dense and well connected, and suggests two rarely used datasets are appropriate for research into sparse DTNs. Finally, the Catora system is proposed to address congestive-driven degradation of service in DTNs by accomplishing two simultaneous tasks: (i) expedite the delivery of higher quality messages by uniquely ordering messages for transfer and delivery, and (ii) avoid congestion through strategic buffer management and message removal. Through dataset-driven simulations, these systems are found to outperform the state-of-the-art, with ChitChat facilitating delivery in sparse DTNs and Catora unencumbered by congestive conditions --Abstract, page iv

    DiSECCS - final summary report. Work packages, 1 - 4

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    Seismic techniques comprise the key geophysical toolset for imaging and characterising induced changes in the subsurface associated with human activity. This ability to observe and quantify changes in fluid saturation, pressure and geological stress and strain using active and passive seismic techniques has critical application to the monitoring of geological CO2 storage. The DiSECCS project (Diagnostic Seismic Toolbox for Efficient Control of CO2 Storage) has developed seismic monitoring tools and methodologies to identify and characterise injectioninduced changes, whether of fluid saturation or pressure, in storage reservoirs. We have developed guidelines for the monitoring systems and protocols required to maintain the integrity of storage reservoirs suitable for large-scale CO2 storage. The focus is on storage in saline aquifers (comprising the largest potential global storage resource), where considerable amounts of in situ water have to be displaced and both pressure and two-phase flow effects have consequences for storage integrity and storage capacity. Underground storage of CO2 is associated with significant levels of public concern. A better understanding of this is a key element of establishing monitoring protocols to instil wider public confidence in CO2 storage. DiSECCS draws on analogue activities, such as ‘fracking’ for shale gas, in conjunction with a discursive process involving lay participants, to gain insights into how people engage with similar underground activities and how controversies surrounding particular projects develop and evolve

    Infrastructure-less D2D Communications through Opportunistic Networks

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    Mención Internacional en el título de doctorIn recent years, we have experienced several social media blackouts, which have shown how much our daily experiences depend on high-quality communication services. Blackouts have occurred because of technical problems, natural disasters, hacker attacks or even due to deliberate censorship actions undertaken by governments. In all cases, the spontaneous reaction of people consisted in finding alternative channels and media so as to reach out to their contacts and partake their experiences. Thus, it has clearly emerged that infrastructured networks—and cellular networks in particular—are well engineered and have been extremely successful so far, although other paradigms should be explored to connect people. The most promising of today’s alternative paradigms is Device-to-Device (D2D) because it allows for building networks almost freely, and because 5G standards are (for the first time) seriously addressing the possibility of using D2D communications. In this dissertation I look at opportunistic D2D networking, possibly operating in an infrastructure-less environment, and I investigate several schemes through modeling and simulation, deriving metrics that characterize their performance. In particular, I consider variations of the Floating Content (FC) paradigm, that was previously proposed in the technical literature. Using FC, it is possible to probabilistically store information over a given restricted local area of interest, by opportunistically spreading it to mobile users while in the area. In more detail, a piece of information which is injected in the area by delivering it to one or more of the mobile users, is opportunistically exchanged among mobile users whenever they come in proximity of one another, progressively reaching most (ideally all) users in the area and thus making the information dwell in the area of interest, like in a sort of distributed storage. While previous works on FC almost exclusively concentrated on the communication component, in this dissertation I look at the storage and computing components of FC, as well as its capability of transferring information from one area of interest to another. I first present background work, including a brief review of my Master Thesis activity, devoted to the design, implementation and validation of a smartphone opportunistic information sharing application. The goal of the app was to collect experimental data that permitted a detailed analysis of the occurring events, and a careful assessment of the performance of opportunistic information sharing services. Through experiments, I showed that many key assumptions commonly adopted in analytical and simulation works do not hold with current technologies. I also showed that the high density of devices and the enforcement of long transmission ranges for links at the edge might counter-intuitively impair performance. The insight obtained during my Master Thesis work was extremely useful to devise smart operating procedures for the opportunistic D2D communications considered in this dissertation. In the core of this dissertation, initially I propose and study a set of schemes to explore and combine different information dissemination paradigms along with real users mobility and predictions focused on the smart diffusion of content over disjoint areas of interest. To analyze the viability of such schemes, I have implemented a Python simulator to evaluate the average availability and lifetime of a piece of information, as well as storage usage and network utilization metrics. Comparing the performance of these predictive schemes with state-of-the-art approaches, results demonstrate the need for smart usage of communication opportunities and storage. The proposed algorithms allow for an important reduction in network activity by decreasing the number of data exchanges by up to 92%, requiring the use of up to 50% less of on-device storage, while guaranteeing the dissemination of information with performance similar to legacy epidemic dissemination protocols. In a second step, I have worked on the analysis of the storage capacity of probabilistic distributed storage systems, developing a simple yet powerful information theoretical analysis based on a mean field model of opportunistic information exchange. I have also extended the previous simulator to compare the numerical results generated by the analytical model to the predictions of realistic simulations under different setups, showing in this way the accuracy of the analytical approach, and characterizing the properties of the system storage capacity. I conclude from analysis and simulated results that when the density of contents seeded in a floating system is larger than the maximum amount which can be sustained by the system in steady state, the mean content availability decreases, and the stored information saturates due to the effects of resource contention. With the presence of static nodes, in a system with infinite host memory and at the mean field limit, there is no upper bound to the amount of injected contents which a floating system can sustain. However, as with no static nodes, by increasing the injected information, the amount of stored information eventually reaches a saturation value which corresponds to the injected information at which the mean amount of time spent exchanging content during a contact is equal to the mean duration of a contact. As a final step of my dissertation, I have also explored by simulation the computing and learning capabilities of an infrastructure-less opportunistic communication, storage and computing system, considering an environment that hosts a distributed Machine Learning (ML) paradigm that uses observations collected in the area over which the FC system operates to infer properties of the area. Results show that the ML system can operate in two regimes, depending on the load of the FC scheme. At low FC load, the ML system in each node operates on observations collected by all users and opportunistically shared among nodes. At high FC load, especially when the data to be opportunistically exchanged becomes too large to be transmitted during the average contact time between nodes, the ML system can only exploit the observations endogenous to each user, which are much less numerous. As a result, I conclude that such setups are adequate to support general instances of distributed ML algorithms with continuous learning, only under the condition of low to medium loads of the FC system. While the load of the FC system induces a sort of phase transition on the ML system performance, the effect of computing load is more progressive. When the computing capacity is not sufficient to train all observations, some will be skipped, and performance progressively declines. In summary, with respect to traditional studies of the FC opportunistic information diffusion paradigm, which only look at the communication component over one area of interest, I have considered three types of extensions by looking at the performance of FC: over several disjoint areas of interest; in terms of information storage capacity; in terms of computing capacity that supports distributed learning. The three topics are treated respectively in Chapters 3 to 5.This work has been supported by IMDEA Networks InstitutePrograma de Doctorado en Ingeniería Telemática por la Universidad Carlos III de MadridPresidente: Claudio Ettori Casetti.- Secretario: Antonio de la Oliva Delgado.- Vocal: Christoph Somme

    Improving Message Dissemination in Opportunistic Networks

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    Data transmission has become a need in various fields, like in social networks with the diverse interaction applications, or in the scientific and engineering areas where for example the use of sensors to capture data is growing, or in emergency situations where there is the imperative need to have a communication system to coordinate rescue operations. Wireless networks have been able to solve these issues to a great extent, but what can we do when a fixed supporting infrastructure is not available or becomes inoperative because of saturation? Opportunistic wireless networks are an alternative to consider in these situations, since their operation does not depend on the existence of a telecommunications infrastructure but they provide connectivity through the organized cooperation of users. This research thesis focuses on these types of networks and is aimed at improving the dissemination of information in opportunistic networks analyzing the main causes that influence the performance of data transmission. Opportunistic networks do not depend on a fixed topology but depend on the number and mobility of users, the type and quantity of information generated and sent, as well as the physical characteristics of the mobile devices that users have to transmit the data. The combination of these elements impacts on the duration of the contact time between mobile users, directly affecting the information delivery probability. This thesis starts by presenting a thorough "state of the art" study where we present the most important contributions related to this area and the solutions offered for the evaluation of the opportunistic networks, such as simulation models, routing protocols, simulation tools, among others. After offering this broad background, we evaluate the consumption of the resources of the mobile devices that affect the performance of the the applications of opportunistic networks, both from the energetic and the memory point of view. Next, we analyze the performance of opportunistic networks considering either pedestrian and vehicular environments. The studied approaches include the use of additional fixed nodes and different data transmission technologies, to improve the duration of the contact between mobile devices. Finally, we propose a diffusion scheme to improve the performance of data transmission based on extending the duration of the contact time and the likelihood that users will collaborate in this process. This approach is complemented by the efficient management of the resources of the mobile devices.La transmisión de datos se ha convertido en una necesidad en diversos ámbitos, como en las redes sociales con sus diversas aplicaciones, o en las áreas científicas y de ingeniería donde, por ejemplo, el uso de sensores para capturar datos está creciendo, o en situaciones de emergencia donde impera la necesidad de tener un sistema de comunicación para coordinar las operaciones de rescate. Las redes inalámbricas actuales han sido capaces de resolver estos problemas en gran medida, pero ¿qué podemos hacer cuando una infraestructura de soporte fija no está disponible o estas se vuelven inoperantes debido a la saturación de peticiones de red? Las redes inalámbricas oportunísticas son una alternativa a considerar en estas situaciones, ya que su funcionamiento no depende de la existencia de una infraestructura de telecomunicaciones sino que la conectividad es a través de la cooperación organizada de los usuarios. Esta tesis de investigación se centra en estos tipos de redes oportunísticas y tiene como objetivo mejorar la difusión de información analizando las principales causas que influyen en el rendimiento de la transmisión de datos. Las redes oportunísticas no dependen de una topología fija, sino que dependen del número y la movilidad de los usuarios, del tipo y cantidad de información generada y enviada, así como de las características físicas de los dispositivos móviles que los usuarios tienen para transmitir los datos. La combinación de estos elementos influye en la duración del tiempo de contacto entre usuarios móviles, afectando directamente a la probabilidad de entrega de información. Esta tesis comienza presentando un exhaustivo estudio del ``estado del arte", donde presentamos las contribuciones más importantes relacionadas con esta área y las soluciones existentes para la evaluación de las redes oportunísticas, tales como modelos de simulación, protocolos de enrutamiento, herramientas de simulación, entre otros. Tras ofrecer esta amplia compilación de investigaciones, se evalúa el consumo de recursos de los dispositivos móviles que afectan al rendimiento de las aplicaciones de redes oportunísticas, desde el punto de vista energético así como de la memoria. A continuación, analizamos el rendimiento de las redes oportunísticas considerando tanto los entornos peatonales como vehiculares. Los enfoques estudiados incluyen el uso de nodos fijos adicionales y diferentes tecnologías de transmisión de datos, para mejorar la duración del contacto entre dispositivos móviles. Finalmente, proponemos un esquema de difusión para mejorar el rendimiento de la transmisión de datos basado en la extensión de la duración del tiempo de contacto, y de la probabilidad de que los usuarios colaboren en este proceso. Este enfoque se complementa con la gestión eficiente de los recursos de los dispositivos móviles.La transmissió de dades s'ha convertit en una necessitat en diversos àmbits, com ara en les xarxes socials amb les diverses aplicacions d'interacció, o en les àrees científiques i d'enginyeria, en les quals, per exemple, l'ús de sensors per a capturar dades creix en l'actualitat, o en situacions d'emergència en què impera la necessitat de tenir un sistema de comunicació per a coordinar les operacions de rescat. Les xarxes sense fil han sigut capaces de resoldre aquests problemes en gran manera, però què podem fer quan una infraestructura de suport fixa no està disponible, o bé aquestes es tornen inoperants a causa de la saturació de peticions de xarxa? Les xarxes sense fil oportunistes són una alternativa que cal considerar en aquestes situacions, ja que el funcionament d'aquestes xarxes no depèn de l'existència d'una infraestructura de telecomunicacions, sinó que la connectivitat s'hi aconsegueix a través de la cooperació organitzada dels usuaris. Aquesta tesi de recerca se centra en aquest tipus de xarxes, i té com a objectiu millorar la difusió d'informació en xarxes oportunistes tot analitzant les principals causes que influeixen en el rendiment de la transmissió de dades. Les xarxes oportunistes no depenen d'una topologia fixa, sinó del nombre i la mobilitat dels usuaris, del tipus i la quantitat d'informació generada i enviada, i de les característiques físiques dels dispositius mòbils que els usuaris tenen per a transmetre les dades. La combinació d'aquests elements influeix en la durada del temps de contacte entre usuaris mòbils, i afecta directament la probabilitat de lliurament d'informació. Aquesta tesi comença amb un estudi exhaustiu de l'estat de la qüestió, en què presentem les contribucions més importants relacionades amb aquesta àrea i les solucions oferides per a l'avaluació de les xarxes oportunistes, com ara models de simulació, protocols d'encaminament o eines de simulació, entre d'altres. Després de mostrar aquest ampli panorama, s'avalua el consum dels recursos dels dispositius mòbils que afecten l'acompliment de les aplicacions de xarxes oportunistes, tant des del punt de vista energètic com de la memòria. A continuació, analitzem l'acompliment de xarxes oportunistes considerant tant els entorns de vianants com els vehiculars. Els enfocaments estudiats inclouen l'ús de nodes fixos addicionals i diferents tecnologies de transmissió de dades per a millorar la durada del contacte entre dispositius mòbils. Finalment, proposem un esquema de difusió per a millorar el rendiment de la transmissió de dades basat en l'extensió de la durada del temps de contacte, i de la probabilitat que els usuaris col·laboren en aquest procés. Aquest enfocament es complementa amb la gestió eficient dels recursos dels dispositius mòbils.Herrera Tapia, J. (2017). Improving Message Dissemination in Opportunistic Networks [Tesis doctoral no publicada]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/86129TESI

    AI Solutions for MDS: Artificial Intelligence Techniques for Misuse Detection and Localisation in Telecommunication Environments

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    This report considers the application of Articial Intelligence (AI) techniques to the problem of misuse detection and misuse localisation within telecommunications environments. A broad survey of techniques is provided, that covers inter alia rule based systems, model-based systems, case based reasoning, pattern matching, clustering and feature extraction, articial neural networks, genetic algorithms, arti cial immune systems, agent based systems, data mining and a variety of hybrid approaches. The report then considers the central issue of event correlation, that is at the heart of many misuse detection and localisation systems. The notion of being able to infer misuse by the correlation of individual temporally distributed events within a multiple data stream environment is explored, and a range of techniques, covering model based approaches, `programmed' AI and machine learning paradigms. It is found that, in general, correlation is best achieved via rule based approaches, but that these suffer from a number of drawbacks, such as the difculty of developing and maintaining an appropriate knowledge base, and the lack of ability to generalise from known misuses to new unseen misuses. Two distinct approaches are evident. One attempts to encode knowledge of known misuses, typically within rules, and use this to screen events. This approach cannot generally detect misuses for which it has not been programmed, i.e. it is prone to issuing false negatives. The other attempts to `learn' the features of event patterns that constitute normal behaviour, and, by observing patterns that do not match expected behaviour, detect when a misuse has occurred. This approach is prone to issuing false positives, i.e. inferring misuse from innocent patterns of behaviour that the system was not trained to recognise. Contemporary approaches are seen to favour hybridisation, often combining detection or localisation mechanisms for both abnormal and normal behaviour, the former to capture known cases of misuse, the latter to capture unknown cases. In some systems, these mechanisms even work together to update each other to increase detection rates and lower false positive rates. It is concluded that hybridisation offers the most promising future direction, but that a rule or state based component is likely to remain, being the most natural approach to the correlation of complex events. The challenge, then, is to mitigate the weaknesses of canonical programmed systems such that learning, generalisation and adaptation are more readily facilitated
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