Resources Optimization For Distributed Mobile Platforms In Smart Cities

This thesis is focused on the study and design of techniques able to optimize resources in distributed mobile platforms. It is related to a smart city environment, in order to enhance quality, performance and interactivity of urban services. The subject is the computation offloading, intended as the delegation of certain computing tasks to an external platform, such as a cloud or a cluster of devices. Offloading the computation tasks can effectively expand the usability of mobile devices beyond their physical limits and may be necessary due to limitations of a system handling a particular task on its own. The computation offloading within an ecosystem as a urban community, where a large amount of users are connected towards even multiple devices, is a challenging subject. In a very close future, smart cities will be peculiar sources of intensive computing tasks, since they are conceived as systems where e-governance will be not only transparent and fast, but also oriented to energy and water conservation, efficient waste disposal, city automation, seamless facilities to travel and affordable access to health management systems. Also traffic will need to be monitored intelligently, emergencies foreseen and resolved quickly, homes and citizens provided with a wide series of control and security devices. All these ambitious aspirations will require the deployment of infrastructures and systems where devices will generate massive data and should be orchestrated in a collective way. In this context, the computation offloading is an operation dealing with the optimization of urban services, in order to reduce costs and consumption of resources and to improve the connection between citizens and government. This dissertation is organized in three main parts, dealing with the optimization of the resources in a smart city from different points of view

5G and beyond networks

This chapter investigates the Network Layer aspects that will characterize the merger of the cellular paradigm and the IoT architectures, in the context of the evolution towards 5G-and-beyond, including some promising emerging services as Unmanned Aerial Vehicles or Base Stations, and V2X communications

Optimisation de la gestion des interférences inter-cellulaires et de l'attachement des mobiles dans les réseaux cellulaires LTE

Driven by an exponential growth in mobile broadband-enabled devices and a continue dincrease in individual data consumption, mobile data traffic has grown 4000-fold over the past 10 years and almost 400-million-fold over the past 15 years. Homogeneouscellular networks have been facing limitations to handle soaring mobile data traffic and to meet the growing end-user demand for more bandwidth and betterquality of experience. These limitations are mainly related to the available spectrumand the capacity of the network. Telecommunication industry has to address these challenges and meet exploding demand. At the same time, it has to guarantee a healthy economic model to reduce the carbon footprint which is caused by mobile communications.Heterogeneous Networks (HetNets), composed of macro base stations and low powerbase stations of different types, are seen as the key solution to improve spectral efficiency per unit area and to eliminate coverage holes. In such networks, intelligent user association and interference management schemes are needed to achieve gains in performance. Due to the large imbalance in transmission power between macroand small cells, user association based on strongest signal received is not adapted inHetNets as only few users would attach to low power nodes. A technique based onCell Individual Offset (CIO) is therefore required to perform load balancing and to favor some Small Cell (SC) attraction against Macro Cell (MC). This offset is addedto users’ Reference Signal Received Power (RSRP) measurements and hence inducing handover towards different eNodeBs. As Long Term Evolution (LTE) cellular networks use the same frequency sub-bands, mobile users may experience strong inter-cellxv interference, especially at cell edge. Therefore, there is a need to coordinate resource allocation among the cells and minimize inter-cell interference. To mitigate stronginter-cell interference, the resource, in time, frequency and power domain, should be allocated efficiently. A pattern for each dimension is computed to permit especially for cell edge users to benefit of higher throughput and quality of experience. The optimization of all these parameters can also offer gain in energy use. In this thesis,we propose a concrete versatile dynamic solution performing an optimization of user association and resource allocation in LTE cellular networks maximizing a certainnet work utility function that can be adequately chosen. Our solution, based on gametheory, permits to compute Cell Individual Offset and a pattern of power transmission over frequency and time domain for each cell. We present numerical simulations toillustrate the important performance gain brought by this optimization. We obtain significant benefits in the average throughput and also cell edge user through put of40% and 55% gains respectively. Furthermore, we also obtain a meaningful improvement in energy efficiency. This work addresses industrial research challenges and assuch, a prototype acting on emulated HetNets traffic has been implemented.Conduit par une croissance exponentielle dans les appareils mobiles et une augmentation continue de la consommation individuelle des données, le trafic de données mobiles a augmenté de 4000 fois au cours des 10 dernières années et près de 400millions fois au cours des 15 dernières années. Les réseaux cellulaires homogènes rencontrent de plus en plus de difficultés à gérer l’énorme trafic de données mobiles et à assurer un débit plus élevé et une meilleure qualité d’expérience pour les utilisateurs.Ces difficultés sont essentiellement liées au spectre disponible et à la capacité du réseau.L’industrie de télécommunication doit relever ces défis et en même temps doit garantir un modèle économique pour les opérateurs qui leur permettra de continuer à investir pour répondre à la demande croissante et réduire l’empreinte carbone due aux communications mobiles. Les réseaux cellulaires hétérogènes (HetNets), composés de stations de base macro et de différentes stations de base de faible puissance,sont considérés comme la solution clé pour améliorer l’efficacité spectrale par unité de surface et pour éliminer les trous de couverture. Dans de tels réseaux, il est primordial d’attacher intelligemment les utilisateurs aux stations de base et de bien gérer les interférences afin de gagner en performance. Comme la différence de puissance d’émission est importante entre les grandes et petites cellules, l’association habituelle des mobiles aux stations de bases en se basant sur le signal le plus fort, n’est plus adaptée dans les HetNets. Une technique basée sur des offsets individuelles par cellule Offset(CIO) est donc nécessaire afin d’équilibrer la charge entre les cellules et d’augmenter l’attraction des petites cellules (SC) par rapport aux cellules macro (MC). Cette offset est ajoutée à la valeur moyenne de la puissance reçue du signal de référence(RSRP) mesurée par le mobile et peut donc induire à un changement d’attachement vers différents eNodeB. Comme les stations de bases dans les réseaux cellulaires LTE utilisent les mêmes sous-bandes de fréquences, les mobiles peuvent connaître une forte interférence intercellulaire, en particulier en bordure de cellules. Par conséquent, il est primordial de coordonner l’allocation des ressources entre les cellules et de minimiser l’interférence entre les cellules. Pour atténuer la forte interférence intercellulaire, les ressources, en termes de temps, fréquence et puissance d’émission, devraient être alloués efficacement. Un modèle pour chaque dimension est calculé pour permettre en particulier aux utilisateurs en bordure de cellule de bénéficier d’un débit plus élevé et d’une meilleure qualité de l’expérience. L’optimisation de tous ces paramètres peut également offrir un gain en consommation d’énergie. Dans cette thèse, nous proposons une solution dynamique polyvalente effectuant une optimisation de l’attachement des mobiles aux stations de base et de l’allocation des ressources dans les réseaux cellulaires LTE maximisant une fonction d’utilité du réseau qui peut être choisie de manière adéquate.Notre solution, basée sur la théorie des jeux, permet de calculer les meilleures valeurs pour l’offset individuelle par cellule (CIO) et pour les niveaux de puissance à appliquer au niveau temporel et fréquentiel pour chaque cellule. Nous présentons des résultats des simulations effectuées pour illustrer le gain de performance important apporté par cette optimisation. Nous obtenons une significative hausse dans le débit moyen et le débit des utilisateurs en bordure de cellule avec 40 % et 55 % de gains respectivement. En outre, on obtient un gain important en énergie. Ce travail aborde des défis pour l’industrie des télécoms et en tant que tel, un prototype de l’optimiseur a été implémenté en se basant sur un trafic HetNets émulé

A trust-driven privacy architecture for vehicular ad-hoc networks

Vehicular Ad-Hoc NETworks (VANETs) are an emerging technology which aims to improve road safety by preventing and reducing traffic accidents. While VANETs offer a great variety of promising applications, such as, safety-related and infotainment applications, they remain a number of security and privacy related research challenges that must be addressed. A common approach to security issues widely adopted in VANETs is the use of Public Key Infrastructures (PKI) and digital certificates in order to enable authentication, authorization and confidentiality. These approaches usually rely on a large set of regional Certification Authorities (CAs). Despite the advantages of PKI-based approaches, there are two main problems that arise, i) the secure interoperability among the different and usually unknown- issuing CAs, and ii) the sole use of PKI in a VANET environment cannot prevent privacy related attacks, such as, linking a vehicle with an identifier, tracking vehicles ¿big brother scenario" and user profiling. Additionally, since vehicles in VANETs will be able to store great amounts of information including private information, unauthorized access to such information should be carefully considered. This thesis addresses authentication and interoperability issues in vehicular communications, considering an inter-regional scenario where mutual authentication between nodes is needed. To provide interoperability between vehicles and services among different domains, an Inter-domain Authentication System (AS) is proposed. The AS supplies vehicles with a trusted set of authentication credentials by implementing a near real-time certificate status service. The proposed AS also implements a mechanism to quantitatively evaluate the trust level of a CA, in order to decide on-the-y if an interoperability relationship can be created. This research work also contributes with a Privacy Enhancing Model (PEM) to deal with important privacy issues in VANETs. The PEM consists of two PKI-based privacy protocols: i) the Attribute-Based Privacy (ABP) protocol, and ii) the Anonymous Information Retrieval (AIR) protocol. The ABP introduces Attribute-Based Credentials (ABC) to provide conditional anonymity and minimal information disclosure, which overcome with the privacy issues related to linkability (linking a vehicle with an identifier) and vehicle tracking (big brother scenario). The AIR protocol addresses user profiling when querying Service Providers (SPs), by relying in a user collaboration privacy protocol based on query forgery and permutation; and assuming that neither participant nodes nor SPs could be completely trusted. Finally, the Trust Validation Model (TVM) is proposed. The TVM supports decision making by evaluating entities trust based on context information, in order to provide i) access control to driver and vehicle's private information, and ii) public information trust validation

Energy sustainability of next generation cellular networks through learning techniques

The trend for the next generation of cellular network, the Fifth Generation (5G), predicts a 1000x increase in the capacity demand with respect to 4G, which leads to new infrastructure deployments. To this respect, it is estimated that the energy consumption of ICT might reach the 51% of global electricity production by 2030, mainly due to mobile networks and services. Consequently, the cost of energy may also become predominant in the operative expenses of a Mobile Network Operator (MNO). Therefore, an efficient control of the energy consumption in 5G networks is not only desirable but essential. In fact, the energy sustainability is one of the pillars in the design of the next generation cellular networks. In the last decade, the research community has been paying close attention to the Energy Efficiency (EE) of the radio communication networks, with particular care on the dynamic switch ON/OFF of the Base Stations (BSs). Besides, 5G architectures will introduce the Heterogeneous Network (HetNet) paradigm, where Small BSs (SBSs) are deployed to assist the standard macro BS for satisfying the high traffic demand and reducing the impact on the energy consumption. However, only with the introduction of Energy Harvesting (EH) capabilities the networks might reach the needed energy savings for mitigating both the high costs and the environmental impact. In the case of HetNets with EH capabilities, the erratic and intermittent nature of renewable energy sources has to be considered, which entails some additional complexity. Solar energy has been chosen as reference EH source due to its widespread adoption and its high efficiency in terms of energy produced compared to its costs. To this end, in the first part of the thesis, a harvested solar energy model has been presented based on accurate stochastic Markov processes for the description of the energy scavenged by outdoor solar sources. The typical HetNet scenario involves dense deployments with a high level of flexibility, which suggests the usage of distributed control systems rather than centralized, where the scalability can become rapidly a bottleneck. For this reason, in the second part of the thesis, we propose to model the SBS tier as a Multi-agent Reinforcement Learning (MRL) system, where each SBS is an intelligent and autonomous agent, which learns by directly interacting with the environment and by properly utilizing the past experience. The agents implemented in each SBS independently learn a proper switch ON/OFF control policy, so as to jointly maximize the system performance in terms of throughput, drop rate and energy consumption, while adapting to the dynamic conditions of the environment, in terms of energy inflow and traffic demand. However, MRL might suffer the problem of coordination when finding simultaneously a solution among all the agents that is good for the whole system. In consequence, the Layered Learning paradigm has been adopted to simplify the problem by decomposing it in subtasks. In particular, the global solution is obtained in a hierarchical fashion: the learning process of a subtask is aimed at facilitating the learning of the next higher subtask layer. The first layer implements an MRL approach and it is in charge of the local online optimization at SBS level as function of the traffic demand and the energy incomes. The second layer is in charge of the network-wide optimization and it is based on Artificial Neural Networks aimed at estimating the model of the overall network.Con la llegada de la nueva generación de redes móviles, la quinta generación (5G), se predice un aumento por un factor 1000 en la demanda de capacidad respecto a la 4G, con la consecuente instalación de nuevas infraestructuras. Se estima que el gasto energético de las tecnologías de la información y la comunicación podría alcanzar el 51% de la producción mundial de energía en el año 2030, principalmente debido al impacto de las redes y servicios móviles. Consecuentemente, los costes relacionados con el consumo de energía pasarán a ser una componente predominante en los gastos operativos (OPEX) de las operadoras de redes móviles. Por lo tanto, un control eficiente del consumo energético de las redes 5G, ya no es simplemente deseable, sino esencial. En la última década, la comunidad científica ha enfocado sus esfuerzos en la eficiencia energética (EE) de las redes de comunicaciones móviles, con particular énfasis en algoritmos para apagar y encender las estaciones base (BS). Además, las arquitecturas 5G introducirán el paradigma de las redes heterogéneas (HetNet), donde pequeñas BSs, o small BSs (SBSs), serán desplegadas para ayudar a las grandes macro BSs en satisfacer la gran demanda de tráfico y reducir el impacto en el consumo energético. Sin embargo, solo con la introducción de técnicas de captación de la energía ambiental, las redes pueden alcanzar los ahorros energéticos requeridos para mitigar los altos costes de la energía y su impacto en el medio ambiente. En el caso de las HetNets alimentadas mediante energías renovables, la naturaleza errática e intermitente de esta tipología de energías constituye una complejidad añadida al problema. La energía solar ha sido utilizada como referencia debido a su gran implantación y su alta eficiencia en términos de cantidad de energía producida respecto costes de producción. Por consiguiente, en la primera parte de la tesis se presenta un modelo de captación de la energía solar basado en un riguroso modelo estocástico de Markov que representa la energía capturada por paneles solares para exteriores. El escenario típico de HetNet supondrá el despliegue denso de SBSs con un alto nivel de flexibilidad, lo cual sugiere la utilización de sistemas de control distribuidos en lugar de aquellos que están centralizados, donde la adaptabilidad podría convertirse rápidamente en un reto difícilmente gestionable. Por esta razón, en la segunda parte de la tesis proponemos modelar las SBSs como un sistema multiagente de aprendizaje automático por refuerzo, donde cada SBS es un agente inteligente y autónomo que aprende interactuando directamente con su entorno y utilizando su experiencia acumulada. Los agentes en cada SBS aprenden independientemente políticas de control del apagado y encendido que les permiten maximizar conjuntamente el rendimiento y el consumo energético a nivel de sistema, adaptándose a condiciones dinámicas del ambiente tales como la energía renovable entrante y la demanda de tráfico. No obstante, los sistemas multiagente sufren problemas de coordinación cuando tienen que hallar simultáneamente una solución de forma distribuida que sea buena para todo el sistema. A tal efecto, el paradigma de aprendizaje por niveles ha sido utilizado para simplificar el problema dividiéndolo en subtareas. Más detalladamente, la solución global se consigue de forma jerárquica: el proceso de aprendizaje de una subtarea está dirigido a ayudar al aprendizaje de la subtarea del nivel superior. El primer nivel contempla un sistema multiagente de aprendizaje automático por refuerzo y se encarga de la optimización en línea de las SBSs en función de la demanda de tráfico y de la energía entrante. El segundo nivel se encarga de la optimización a nivel de red del sistema y está basado en redes neuronales artificiales diseñadas para estimar el modelo de todas las BSsPostprint (published version

Network resource allocation policies with energy transfer capabilities

During the last decades, mobile network operators have witnessed an exponential increase in the traffic demand, mainly due to the high request of services from a huge amount of users. The trend is of a further increase in both the traffic demand and the number of connected devices over the next years. The traffic load is expected to have an annual growth rate of 53% for the mobile network alone, and the upcoming industrial era, which will connect different types of devices to the mobile infrastructure including human and machine type communications, will definitely exacerbate such an increasing trend. The current directions anticipate that future mobile networks will be composed of ultra dense deployments of heterogeneous Base Stations (BSs), where BSs using different transmission powers coexist. Accordingly, the traditional Macro BSs layer will be complemented or replaced with multiple overlapping tiers of small BSs (SBSs), which will allow extending the system capacity. However, the massive use of Information and Communication Technology (ICT) and the dense deployment of network elements is going to increase the level of energy consumed by the telecommunication infrastructure and its carbon footprint on the environment. Current estimations indicates that 10% of the worldwide electricity generation is due to the ICT industry and this value is forecasted to reach 51% by 2030, which imply that 23% of the carbon footprint by human activity will be due to ICT. Environmental sustainability is thus a key requirement for designing next generation mobile networks. Recently, the use of Renewable Energy Sources (RESs) for supplying network elements has attracted the attention of the research community, where the interest is driven by the increased efficiency and the reduced costs of energy harvesters and storage devices, specially when installed to supply SBSs. Such a solution has been demonstrated to be environmentally and economically sustainable in both rural and urban areas. However, RESs will entail a higher management complexity. In fact, environmental energy is inherently erratic and intermittent, which may cause a fluctuating energy inflow and produce service outage. A proper control of how the energy is drained and balanced across network elements is therefore necessary for a self-sustainable network design. In this dissertation, we focus on energy harvested through solar panels that is deemed the most appropriate due to the good efficiency of commercial photovoltaic panels as well as the wide availability of the solar source for typical installations. The characteristics of this energy source are analyzed in the first technical part of the dissertation, by considering an approach based on the extraction of features from collected data of solar energy radiation. In the second technical part of the thesis we introduce our proposed scenario. A federation of BSs together with the distributed harvesters and storage devices at the SBS sites form a micro-grid, whose operations are managed by an energy management system in charge of controlling the intermittent and erratic energy budget from the RESs. We consider load control (i.e., enabling sleep mode in the SBSs) as a method to properly manage energy inflow and spending, based on the traffic demand. Moreover, in the third technical part, we introduce the possibility of improving the network energy efficiency by sharing the exceeding energy that may be available at some BS sites within the micro-grid. Finally, a centralized controller based on supervised and reinforcement learning is proposed in the last technical part of the dissertation. The controller is in charge of opportunistically operating the network to achieve efficient utilization of the harvested energy and prevent SBSs blackout.Durante las últimas décadas, los operadores de redes móviles han sido testigos de un aumento exponencial en la demanda de tráfico, principalmente debido a la gran solicitud de servicios de una gran cantidad de usuarios. La tendencia es un aumento adicional tanto en la demanda de tráfico como en la cantidad de dispositivos conectados en los próximos años. Se espera que la carga de tráfico tenga una tasa de crecimiento anual del 53% solo para la red móvil, y la próxima era industrial, que conectará diferentes tipos de dispositivos a la infraestructura móvil, definitivamente exacerbará tal aumento. Las instrucciones actuales anticipan que las redes móviles futuras estarán compuestas por despliegues ultra densos de estaciones base (BS) heterogéneas. En consecuencia, la capa tradicional de Macro BS se complementará o reemplazará con múltiples niveles superpuestos de pequeños BS (SBS), lo que permitirá ampliar la capacidad del sistema. Sin embargo, el uso masivo de la Tecnología de la Información y la Comunicación (TIC) y el despliegue denso de los elementos de la red aumentará el nivel de energía consumida por la infraestructura de telecomunicaciones y su huella de carbono en el medio ambiente. Las estimaciones actuales indican que el 10% de la generación mundial de electricidad se debe a la industria de las TIC y se prevé que este valor alcance el 51% para 2030, lo que implica que el 23% de la huella de carbono por actividad humana se deberá a las TIC. La sostenibilidad ambiental es, por lo tanto, un requisito clave para diseñar redes móviles de próxima generación. Recientemente, el uso de fuentes de energía renovables (RES) para suministrar elementos de red ha atraído la atención de la comunidad investigadora, donde el interés se ve impulsado por el aumento de la eficiencia y la reducción de los costos de los recolectores y dispositivos de almacenamiento de energía, especialmente cuando se instalan para suministrar SBS. Se ha demostrado que dicha solución es ambiental y económicamente sostenible tanto en áreas rurales como urbanas. Sin embargo, las RES conllevarán una mayor complejidad de gestión. De hecho, la energía ambiental es inherentemente errática e intermitente, lo que puede causar una entrada de energía fluctuante y producir una interrupción del servicio. Por lo tanto, es necesario un control adecuado de cómo se drena y equilibra la energía entre los elementos de la red para un diseño de red autosostenible. En esta disertación, nos enfocamos en la energía cosechada a través de paneles solares que se considera la más apropiada debido a la buena eficiencia de los paneles fotovoltaicos comerciales, así como a la amplia disponibilidad de la fuente solar para instalaciones típicas. Las características de esta fuente de energía se analizan en la primera parte técnica de la disertación, al considerar un enfoque basado en la extracción de características de los datos recopilados de radiación de energía solar. En la segunda parte técnica de la tesis presentamos nuestro escenario propuesto. Una federación de BS junto con los cosechadores distribuidos y los dispositivos de almacenamiento forman una microrred, cuyas operaciones son administradas por un sistema de administración de energía a cargo de controlar el presupuesto de energía intermitente y errático de las RES. Consideramos el control de carga como un método para administrar adecuadamente la entrada y el gasto de energía, en función de la demanda de tráfico. Además, en la tercera parte técnica, presentamos la posibilidad de mejorar la eficiencia energética de la red al compartir la energía excedente que puede estar disponible en algunos sitios dentro de la microrred. Finalmente, se propone un controlador centralizado basado en aprendizaje supervisado y de refuerzo en la última parte técnica de la disertación. El controlador está a cargo de operar la red para lograr una utilización eficiente de energía y previene el apagón de SB

Research on Reliable Low-Power Wide-Area Communications Utilizing Multi-RAT LPWAN Technologies for IoT Applications

Předkládaná disertační práce je zaměřena na „Výzkum spolehlivé komunikace pro IoT aplikace v bezdrátových sítích využívajících technologie Multi-RAT LPWAN“. Navzdory značnému pokroku v oblasti vývoje LPWA technologií umožňující masivní komunikace mezi zařízeními (mMTC), nemusí tyto technologie výkonnostně dostačovat pro nově vznikající aplikace internetu věcí. Hlavním cílem této disertační práce je proto nalezení a vyhodnocení limitů současných LPWA technologií. Na základě těchto dat jsou nevrženy nové mechanismy umožňující snazší plánování a vyhodnocování síťového pokrytí. Navržené nástroje jsou vyladěny a validovány s využitím dat získaných z rozsáhlých měřících kampaních provedených v zákaznických LPWA sítích. Tato disertační práce dále obsahuje návrh LPWA zařízení vybavených více komunikačními rozhraními (multi-RAT) které mohou umožnit překonání výkonnostních limitů jednotlivých LPWA technologií. Současná implementace se zaměřuje zejména na snížení spotřeby zařízení s více rádiovými rozhraními, což je jejich největší nevýhodou. K tomuto účelu je využito algoritmů strojového učení, které jsou schopné dynamicky vybírat nejvhodnější rozhraní k přenosu.This doctoral thesis addresses the “Research on Reliable Low-Power Wide-Area Communications Utilizing Multi-RAT LPWAN Technologies for IoT Applications”. Despite the immense progress in massive Machine-Type Communication (mMTC) technology enablers such as Low-Power Wide-Area (LPWA) networks, their performance does not have to satisfy the requirements of novelty Internet of Things (IoT) applications. The main goal of this Ph.D. work is to explore and evaluate the limitations of current LPWA technologies and propose novel mechanisms facilitating coverage planning and assessment. Proposed frameworks are fine-tuned and cross-validated by the extensive measurement campaigns conducted in public LPWA networks. This doctoral thesis further introduces the novelty approach of multi-RAT LPWA devices to overcome the performance limitation of individual LPWA technologies. The current implementation primarily focuses on diminishing the greatest multi-RAT solutions disadvantage, i.e., increased power consumption by employing a machine learning approach to radio interface selection.

Mecanismos para controlo e gestão de redes 5G: redes de operador

In 5G networks, time-series data will be omnipresent for the monitoring of network metrics. With the increase in the number of Internet of Things (IoT) devices in the next years, it is expected that the number of real-time time-series data streams increases at a fast pace. To be able to monitor those streams, test and correlate different algorithms and metrics simultaneously and in a seamless way, time-series forecasting is becoming essential for the pro-active successful management of the network. The objective of this dissertation is to design, implement and test a prediction system in a communication network, that allows integrating various networks, such as a vehicular network and a 4G operator network, to improve the network reliability and Quality-of-Service (QoS). To do that, the dissertation has three main goals: (1) the analysis of different network datasets and implementation of different approaches to forecast network metrics, to test different techniques; (2) the design and implementation of a real-time distributed time-series forecasting architecture, to enable the network operator to make predictions about the network metrics; and lastly, (3) to use the forecasting models made previously and apply them to improve the network performance using resource management policies. The tests done with two different datasets, addressing the use cases of congestion management and resource splitting in a network with a limited number of resources, show that the network performance can be improved with proactive management made by a real-time system able to predict the network metrics and act on the network accordingly. It is also done a study about what network metrics can cause reduced accessibility in 4G networks, for the network operator to act more efficiently and pro-actively to avoid such eventsEm redes 5G, séries temporais serão omnipresentes para a monitorização de métricas de rede. Com o aumento do número de dispositivos da Internet das Coisas (IoT) nos próximos anos, é esperado que o número de fluxos de séries temporais em tempo real cresça a um ritmo elevado. Para monitorizar esses fluxos, testar e correlacionar diferentes algoritmos e métricas simultaneamente e de maneira integrada, a previsão de séries temporais está a tornar-se essencial para a gestão preventiva bem sucedida da rede. O objetivo desta dissertação é desenhar, implementar e testar um sistema de previsão numa rede de comunicações, que permite integrar várias redes diferentes, como por exemplo uma rede veicular e uma rede 4G de operador, para melhorar a fiabilidade e a qualidade de serviço (QoS). Para isso, a dissertação tem três objetivos principais: (1) a análise de diferentes datasets de rede e subsequente implementação de diferentes abordagens para previsão de métricas de rede, para testar diferentes técnicas; (2) o desenho e implementação de uma arquitetura distribuída de previsão de séries temporais em tempo real, para permitir ao operador de rede efetuar previsões sobre as métricas de rede; e finalmente, (3) o uso de modelos de previsão criados anteriormente e sua aplicação para melhorar o desempenho da rede utilizando políticas de gestão de recursos. Os testes efetuados com dois datasets diferentes, endereçando os casos de uso de gestão de congestionamento e divisão de recursos numa rede com recursos limitados, mostram que o desempenho da rede pode ser melhorado com gestão preventiva da rede efetuada por um sistema em tempo real capaz de prever métricas de rede e atuar em conformidade na rede. Também é efetuado um estudo sobre que métricas de rede podem causar reduzida acessibilidade em redes 4G, para o operador de rede atuar mais eficazmente e proativamente para evitar tais acontecimentos.Mestrado em Engenharia de Computadores e Telemátic