An interactive visualization of self-organizing radio networks is developed. When the size and complexity of today’s radio networks grows, the need of automated network organizing methods increase to cut down on work, money and mistakes. The automation, however, leads the network operators to lose control over their own network and possible trust issues come along. Instead of giving back control to the operators, which would increase costs and work, Ericsson has suggested creating a visualization making clear that their self-organizing methods work as intended and letting the operator to efficiently explore their own network data. In this thesis project a visualization application is developed allowing the network operator to explore the settings and performance of their network organized by Ericsson’s automatic algorithm called Automatic Neighbor Relations (ANR). The user can interact with the visualization by picking, filtering, and more, to find potential patterns in the data, find bad data values, and see how settings affect the performance of the network. The visualization is built around a map where parameter and performance data is presented. Other visualization components come from the visualization framework GeoAnalytics Visualization (GAV), developed at Linköpings universitet, which also stands as a basis for the entire visualization.

<p>An interactive visualization of self-organizing radio networks is developed. When the size and complexity of today’s radio networks grows, the need of automated network organizing methods increase to cut down on work, money and mistakes. The automation, however, leads the network operators to lose control over their own network and possible trust issues come along. Instead of giving back control to the operators, which would increase costs and work, Ericsson has suggested creating a visualization making clear that their self-organizing methods work as intended and letting the operator to efficiently explore their own network data.</p><p>In this thesis project a visualization application is developed allowing the network operator to explore the settings and performance of their network organized by Ericsson’s automatic algorithm called Automatic Neighbor Relations (ANR). The user can interact with the visualization by picking, filtering, and more, to find potential patterns in the data, find bad data values, and see how settings affect the performance of the network.</p><p>The visualization is built around a map where parameter and performance data is presented. Other visualization components come from the visualization framework GeoAnalytics Visualization (GAV), developed at Linköpings universitet, which also stands as a basis for the entire visualization.</p>

Given a set of peers with overlapping interests where each peer wishes to keep track of new documents that are relevant to their interests, we propose Shrack-a self-organizing peer-to-peer (P2P) system for document sharing and tracking. The goal of a document-tracking system is to disseminate new documents as they are published. We present a framework of Shrack and propose a gossip-like pull-only information dissemination protocol. We explore and develop mechanisms to enable a self-organizing network, based on common interest of document sets among peers. Shrack peers collaboratively share new documents of interest with other peers. Interests of peers are modeled using relevant document sets and are represented as peer profiles. There is no explicit pro file exchange between peers and no global information available. We describe how peers create their user pro files, discover the existence of other peers, locally learn about interest of other peers, and finally form a self-organizing overlay network of peers with common interests. Unlike most existing P2P file sharing systems which serve their users by finding relevant documents based on an instant query, Shrack is designed to help users that have long-term interests to keep track of relevant documents that are newly available in the system. The framework can be used as an infrastructure for any kind of documents and data, but in this thesis, we focus on research publications. We built an event-driven simulation to evaluate the performance and behaviour of Shrack. We model simulated users associated with peers after a subset of authors in the ACM digital library metadata collection. The experimental results demonstrate that the Shrack dissemination protocol is scalable as the network size increases. In addition, self-organizing overlay networks, where connections between peers are based on common interests as captured by their associated document sets, can help improve the relevance of documents received by peers in terms of F-score over random peer networks. Moreover, the resulting self-organizing networks have the characteristics of social networks.

Mobile cellular networks are widely integrated in today’s infrastructure. These networks are constantly evolving and continuously expanding, especially with the introduction of fifth-generation (5G). It is important to ensure the effectiveness of these expansions.Mobile networks consist of a set of radio nodes that are distributed in a geographicalregion to provide connectivity services. Each radio node is served by a set of cells. Thehandover relations between cells is determined by Software features such as AutomaticNeighbor Relations (ANR). The handover relations, also refereed as edges, betweenradio nodes in the mobile network graph are created through historical interactions between User Equipment (UE) and radio nodes. The method has the limitation of not being able to set the edges before the physical hardware is integrated. In this work, we usegraph-based deep learning methods to determine mobility relations (edges), trained onradio node configuration data and a set of reliable relations of ANR in stable networks.The report focuses on measuring the accuracy and precision of different graph baseddeep learning approaches applied to real-world mobile networks. The report considers four models. Our comprehensive experiments on Telecom datasets obtained fromoperational Telecom Networks demonstrate that graph neural network model and multilayer perceptron trained with Binary Cross Entropy (BCE) loss outperform all othermodels. The four models evaluation showed that considering graph structure improveresults. Additionally, the model investigates the use of heuristics to reduce the trainingtime based on distance between radio node to eliminate irrelevant cases. The use ofthese heuristics improved precision and accuracy.

Ce document est dédié à mes travaux de recherche développés au cours des six dernières années sur la conception et l'évaluation de systèmes de réseaux sans fil et est le résultat d'un certain nombre de collaborations. En particulier, mon objectif principal a été le soutien à la livraison fiable de données dans les réseaux sans fil auto-organisés. La question centrale, qui a guidée mes activités de recherche, est la suivante: "quels sont les services réseaux sous-jacents à la bonne conception de stratégies de communication sans fil dans les systèmes de réseaux auto-organisés (fixe ou mobile)?". Les réseaux auto-organisés (WSONs) ont des caractéristiques intrinsèques et, par conséquent, nécessitent des solutions particulières qui les distinguent des réseaux traditionnels basés sur des graphes. Les différents types de WSONs nécessitent des services adaptatifs ciblés pour faire face à leur nature (i.e., la mobilité, la limitation des ressources, le manque de fiabilité des communications sans fil,. . .) et pour trouver une adéquation entre leur fonctionnement et l'environnement. Influencée par de telles observations, mes activités de recherche ont été guidées par l'objectif principal de fournir au niveau du réseau un soutien à la livraison fiable de données dans les réseaux sans fil auto-organisés. Les axes de recherche, que j'ai développés avec mes collègues dans ce contexte, sont classés comme étant des services adaptifs "au niveau noeud" et "au niveau réseau" et se distinguent par le niveau auquel l'adaptation est considérée. Mes contributions, liées à la première catégorie de service, reposent sur les services de localisation et de découverte de voisinage. En raison de la limitation de page, ce manuscrit est, cependant, consacré à la recherche que j'ai menée autour des services adaptatifs au niveau du réseau. Par conséquent, il est structuré en trois chapitres principaux correspondants à trois classes de services réseaux : des services de gestion de la topologie, des services de gestion des données et des services de routage et d'acheminement. Ma première contribution concerne des services de gestion de la topologie, qui sont réalisés grâce à l'adaptation des noeuds - en imposant une hiérarchie dans le réseau via la clusterisation ou en supprimant des noeuds du graphe du réseau en les éteignant - et par la mobilité contrôlée - qui affecte à la fois la présence de noeuds et de liens, ainsi que la qualité des liens dans le graphe du réseau. Se basant sur l'adaptation de noeuds, le protocole SAND, les systèmes VINCOS et NetGeoS qui portent respectivement sur la conservation d'énergie et sur l'auto-structuration des réseaux de capteurs sans fil (WSN) ont été proposés. Ensuite, se basant sur la mobilité contrôlée, des propositions, liées à la conception de trajectoire de Hilbert et du protocole Cover, ont été présentées. Elles se concentrent sur le déploiement de solutions pour la couverture de zone avec des noeuds mobiles et ont été conçues pour surveiller périodiquement une zone géographique ou pour couvrir des noeuds de capteurs mobiles (cibles). Considérant les services de gestion de données, mes contributions se rapportent à la collecte des données - qui implique des solutions de distribution de données avec des objectifs liés a l'organisation - et la diffusion des données - où les flux de données sont dirigés vers le réseau. Pour cela, les protocoles DEEP et Supple ont été conçus pour les réseaux de capteurs sans fil, tandis que FairMix et VIP delegation se concentrent sur la diffusion d'information dans les réseaux sans fil sociaux. En particulier, afin d'améliorer la diffusion des données, FairMix et VIP delegation, exploitent les similarités des intérêts sociaux des personnes ou des groupes dans les réseaux fixes ou l'aspect social de leurs interactions sans fil dans les réseaux mobiles. Finalement, mes travaux sur les services adaptatifs d'acheminement attaquent la problèmatique de la connectivité opportuniste dans les réseaux sans fil tolérants aux délais. Dans ce contexte, les protocoles Seeker et GrAnt ont été conçus et utilisent respectivement l'histoire du contact entre les noeuds (les schémas de contact et de communication) et les propriétés des réseaux sociaux de noeuds afin de prédire les futures rencontres et de mieux ajuster les décisions de transfert. Au regard des nouvelles possibilités de communication et du changement dynamique observé au cours des dernières années dans les réseaux sans fil, mes activités de recherche se sont progressivement orientés des réseaux auto-organisés connectés vers les réseaux connectés par intermittence et opportunistes. De cette façon, mes perspectives de recherche future sont: (1) tirer profit des schémas de mobilité incontrôlée des dispositifs mobiles pervasifs pour améliorer les efforts de perception collaborative; (2) regarder plus en profondeur les techniques de génération de graphes sociaux à partir des traces décrivant les contacts entre les noeuds; (3) étudier quels sont les facteurs ayant un impact (positif ou négatif) sur le succès de la diffusion de l'information dans les réseaux sociaux mobiles, et (4) étudier la possibilité d'adapter le codage réseau à la diffusion d'information dans les réseaux sociaux mobiles.

Submitted by Renata Lopes (renatasil82@gmail.com) on 2018-08-22T18:37:28Z No. of bitstreams: 1 lucassaarcerqueira.pdf: 1610432 bytes, checksum: a5ce4c7b10b86e4e43966b9b9268e608 (MD5) / Approved for entry into archive by Adriana Oliveira (adriana.oliveira@ufjf.edu.br) on 2018-09-03T16:34:40Z (GMT) No. of bitstreams: 1 lucassaarcerqueira.pdf: 1610432 bytes, checksum: a5ce4c7b10b86e4e43966b9b9268e608 (MD5) / Made available in DSpace on 2018-09-03T16:34:40Z (GMT). No. of bitstreams: 1 lucassaarcerqueira.pdf: 1610432 bytes, checksum: a5ce4c7b10b86e4e43966b9b9268e608 (MD5) Previous issue date: 2018-03-27 / CAPES - Coordenação de Aperfeiçoamento de Pessoal de Nível Superior / O monitoramento de ambientes aquáticos ainda é uma tarefa difícil e dispendiosa. De fato, em ambientes aquáticos, ondas eletromagnéticas e ópticas sofrem alta atenuação e, mesmo a comunicação acústica apresenta baixa vazão e alta taxa de erro de bits. A maioria das abordagens existentes para melhorar o desempenho da comunicação subaquática se baseia no desenvolvimento de modems acústicos, acesso múltiplo ao canal de comunicação e roteamento de dados. Neste trabalho apresentamos PCRA: um Protocolo Cooperativo para Redes de Sensores Aquáticas. O PCRA funciona de forma síncrona/assíncrona sobre o método TDMA combinado com um esquema ARQ baseado em Selective Repeat. Cada nó que não possui dados para transmitir pode se tornar um cooperador e retransmitir mensagens para auxiliar os nós vizinhos. Ele usa os nós sensores ociosos como nós retransmissores, aumentando a diversidade do espaço de comunicação. Nossas simulações mostram que, quando comparado a um protocolo não cooperativo, o PCRA reduz a taxa de erro de pacotes em 65% e aumenta o goodput em 16% enquanto gasta menos de 1% a mais de energia. / Monitoring underwater environments is still a hard and costly task. Indeed, electromagnetic and optical waves suffer high attenuation, being absorbed in a few meters and even acoustic communication presents low throughput and high bit error rate. Most of the existing approaches to enhance underwater communication performance relies on developing acoustic modems, multiple access of the communication channel, and data routing. In this paper we present PCRA: a Cooperative Protocol for Underwater Sensor Networks. PCRA synchronously/asynchronously works on top of TDMA method combined with an ARQ scheme based on selective repeat technique. Each node that has no data to transmit can become a cooperator and retransmit messages to assist neighboring nodes. It uses idle sensor nodes as relay nodes, enhancing communication space diversity. Our simulations show that, when compared to a non-cooperative protocol, PCRA enhances overall network performance metrics. For instance, it reduces packet error rate by 65% and increases goodput by 16% while spending less than 1% more energy.

[pt] Os sistemas de comunicações móveis atuais vêm enfrentando novos desafios, marcados pelo aumento do uso de novos dispositivos e pela mudança nos padrões de consumo de banda causada pelas aplicações emergentes. É por isso que a indústria de comunicações e a comunidade acadêmica vêm trabalhando tanto nas dificuldades apresentadas nas redes móveis atuais quanto nos desafios técnicos para o desenvolvimento dos esperados sistemas de quinta geração (5G). O grande aumento dos elementos da rede de acesso rádio e a implementação de cenários heterogêneos (macro e pico eNBs, Relay Nodes, etc.) são duas das principais abordagens utilizadas para melhorar a capacidade da rede. No entanto, esse acréscimo de elementos ou, densificação, traz consigo um aumento nos custos e na complexidade nas tarefas de operação e gerenciamento do sistema, já que os novos elementos de rede precisam ser adaptados, configurados e gerenciados continuamente para garantir e aumentar a eficiência da rede, melhorando a qualidade nos serviços oferecidos aos usuários. Este trabalho de pesquisa propõe a inclusão de mecanismos cognitivos, incluindo técnicas de adaptação, nas arquiteturas das redes de acesso móvel. O trabalho propõe igualmente novos mecanismos de auto-organização (Self Organizing Networks, SON) para o balanceamento de carga, empregando modelos dinâmicos capazes de tomar decisões inteligentes e aprender a partir de experiências para atingir os objetivos de desempenho desejados. / [en] Today s mobile communications systems are facing new challenges, triggered by the increased use of new devices and the growth of bandwidth hungry applications. This is why over the last years, the telecommunication industry and academic communities have been focused on research and development of technologies for the upcoming 5th generation mobile systems (5G). Among the potential candidates, network densification has attracted growing attention as a key mechanism to fulfill the objective proposed in 5G, by increasing the number of radio-base stations (on the coverage area) and introducing an additional layer of low-power access nodes (e.g., Femto, picocells, relay nodes). However, this approach has also posed new challenges in network configuration, management, and optimization tasks to ensure and increase the mobile network efficiency. This research proposes the inclusion of cognitive mechanisms and adaptive techniques in the architectures of mobile radio access networks. This work also proposes new self-organizing (SON) functions for load balancing, enhanced with capabilities of learning from previous experiences to achieve future desired performance goals.

Le réseau mobile d’Orange France comprend plus de 100 000 antennes 2G, 3G et 4G sur plusieurs bandes de fréquences sans compter les nombreuses femto-cells fournies aux clients pour résoudre les problèmes de couverture. Ces chiffres ne feront que s’accroître pour répondre à la demande sans cesse croissante des clients pour les données mobiles. Cela illustre le défi énorme que rencontrent les opérateurs de téléphonie mobile en général à savoir gérer un réseau aussi complexe tout en limitant les coûts d’opération pour rester compétitifs. Cette thèse s’attache à utiliser le concept SON (réseaux auto-organisants) pour réduire cette complexité en automatisant les tâches répétitives ou complexes. Plus spécifiquement, nous proposons des algorithmes d’optimisation automatique pour des scénarios liés à la densification par les small cells ou les antennes actives. Nous abordons les problèmes classiques d’équilibrage de charge mais avec un lien backhaul à capacité limitée et de coordination d’interférence que ce soit dans le domaine temporel (notamment avec le eICIC) ou le domaine fréquentiel. Nous proposons aussi des algorithmes d’activation optimale de certaines fonctionnalités lorsque cette activation n’est pas toujours bénéfique. Pour la formulation mathématique et la résolution de tous ces algorithmes, nous nous appuyons sur les résultats de l’approximation stochastique et de l’optimisation convexe. Nous proposons aussi une méthodologie systématique pour la coordination de multiples fonctionnalités SON qui seraient exécutées en parallèle. Cette méthodologie est basée sur les jeux concaves et l’optimisation convexe avec comme contraintes des inégalités matricielles linéaires. / The mobile network of Orange in France comprises more than 100 000 2G, 3G and 4G antennas with severalfrequency bands, not to mention many femto-cells for deep-indoor coverage. These numbers will continue toincrease in order to address the customers’ exponentially increasing need for mobile data. This is an illustrationof the challenge faced by the mobile operators for operating such a complex network with low OperationalExpenditures (OPEX) in order to stay competitive. This thesis is about leveraging the Self-Organizing Network(SON) concept to reduce this complexity by automating repetitive or complex tasks. We specifically proposeautomatic optimization algorithms for scenarios related to network densification using either small cells orActive Antenna Systems (AASs) used for Vertical Sectorization (VeSn), Virtual Sectorization (ViSn) and multilevelbeamforming. Problems such as load balancing with limited-capacity backhaul and interference coordination eitherin time-domain (eICIC) or in frequency-domain are tackled. We also propose optimal activation algorithms forVeSn and ViSn when their activation is not always beneficial. We make use of results from stochastic approximationand convex optimization for the mathematical formulation of the problems and their solutions. We also proposea generic methodology for the coordination of multiple SON algorithms running in parallel using results fromconcave game theory and Linear Matrix Inequality (LMI)-constrained optimization.