行動上網之概念已提出多年，然受制於硬體規格限制與資訊實用面廣度、深度不足，因此市場未如預期般快速成長。如今受惠於系統業者廣佈GPRS系統，功能性手機普及化，以及內容提供者積極參與，行動數據服務始展露一絲曙光。和信電訊公司看好市場前景，藉與NTT DoCoMo合資關係引進行動上網服務典範─i-mode。本研究之主要目的旨在檢視目前台灣i-mode服務之品質，經結合理論探討與實務分析，具體提出發展行動數據服務之改善要素。 本研究彌補國內相關研究在深度上之不足，採質性研究法，以NTT DoCoMo發展i-mode服務為模式，就技術、內容、策略和使用之關鍵面向，深入訪談系統業者、內容提供者和使用者後，交叉分析以找出三者認知上之落差，進而推論改善行動數據服務品質之關鍵要素，並提出建議解決方案。 本研究結果發現：系統業者之改善要素包括價格、手機選擇、連線品質、行銷、後端管理及利潤分享；內容提供者之改善要素包括價格、實用性、介面、品牌及獲利模式。本研究最大貢獻亦在於提供相關業者諸多建言及產業發展方針，整體而論：第一，系統業者應致力於提升行動上網平台之可用性；第二，內容提供者宜鎖定目標客群主動創造需求；第三，系統業者和內容提供者應從商業合作關係提升為策略聯盟關係。在延伸討論部份，本研究認為：第一，應建立以內容聚集為中心之商務模式架構；第二，推動XHTML做為改善非人性化介面和網路封閉性之解決方案；第三，將行動數據服務從以個人為單位提升到社會關係的層次；第四，開發企業用途之應用，將服務拓展至企業市場。 / The concept of mobile internet has been introduced many years ago; however, due to the limits of hardware standards and the inadequacies of breadth and depth of information practicability, the market didn’t rise and flourish as expectations. Now because of the benefits of distributions of GPRS network system, popularization of feature cellular phones, and active participation of content providers, the mobile data services start to grow. With faith in good prospects of the market, the KGT company introduced i-mode, the paradigm of mobile internet services, from NTT DoCoMo through their partnership. The main purposes of this research are to view the quality of recent i-mode service in Taiwan, and to concretely propose key improving factors of developing mobile data services through integration of theory reviews and practice analyses. For retrieving the lacks of depth in former domestic researches, this research uses the i-mode service as an example and adopts qualitative research methods by deeply interviewing with the operator, the content providers, as well as the users on critical aspects of the technology, the content, the strategy, and the usage. Then, after cross-analyzing to find gaps of the cognition among the three roles, this research infers the key factors to improve the quality of mobile data services and proposes the corresponding solutions. The major findings of this research are as follows. The improving factors of operators includes the price, the phones selectivity, the connection quality, the marketing, and the profits share. Those of content providers includes the price, the practicability, the interface, the brand, and the profit-earning model. This thesis also contributes many suggestions and guidelines of industry development for relevant players. First, operators should strive for enhancing the usability of mobile internet platforms. Second, content providers ought to aim at target customers and actively create demands. Third, the relationship of operators and content providers must be promoted from business cooperation to strategic alliance. In the section of extended discussions, this research presents several recommendations. First, a framework of content aggregation-centered business models should be established. Second, XHTML should be drived as a solution to improve recent unfriendly interfaces and network closure. Third, mobile data services based on individuals should be raised to a high level based on social relationships. Fourth, services should be expanded to cooperate market by developing applications for enterprise purposes.

A incorporação das tecnologias de computação e de comunicação nos veículos modernos está viabilizando uma nova geração de automóveis conectados. Com a capacidade de se organizarem em rede, nas chamadas redes veiculares ad hoc (VANETs), eles poderão, num futuro próximo, (i) tornar o trânsito mais seguro para os motoristas, passageiros e pedestres e/ou (ii) promover uma experiência de transporte mais agradável, com maior conforto. É neste contexto que se destaca o Sensoriamento Veicular Colaborativo (VCS), um paradigma emergente e promissor que explora as tecnologias já embarcados nos próprios veículos para a obtenção de dados in loco. O VCS tem demonstrado ser um modelo auspicioso para o desenvolvimento e implantação dos Sistemas Inteligentes de Transporte (ITSs). Ocorre, todavia, que, em grandes centros urbanos, dependendo do fenômeno a ser monitorado, as aplicações de VCS podem gerar um tráfego de dados colossal entre os veículos e o centro de monitoramento. Considerando que as informações dos automóveis são geralmente enviadas para um servidor remoto usando as infraestruturas das redes móveis, o número massivo de transmissões geradas durante as atividades de sensoriamento pode sobrecarregá-las e degradar consideravelmente a Qualidade de Serviço (QoS) que elas oferecem. Este documento de tese descreve e analisa uma abordagem de clusterização geográfica que se apoia no uso de comunicações Veículo-para-Veículo (V2V) para promover o descarregamento de dados do VCS em redes celulares, de forma a minimizar os impactos supracitados. Os resultados experimentais obtidos mostraram que o uso das comunicações V2V como método complementar de aquisição de dados in loco foi capaz de diminuir consideravelmente a quantidade transmissões realizadas sobre as redes móveis, sem a necessidade de implantação de novas infraestruturas de comunicação no ambiente, e com um reduzido atraso médio adicional fim a fim na obtenção das informações. A abordagem desenvolvida também se apresenta como uma plataforma de software flexível sobre a qual podem ser incorporadas técnicas de agregação de dados, o que possibilitaria aumentar ainda mais a preservação dos recursos de uplink das redes celulares. Considerando que a era da Internet das Coisas (IoT) e das cidades inteligentes está apenas começando, soluções para o descarregamento de dados, tal como a tratada nesta pesquisa, são consideradas imprescindíveis para continuar mantendo a rede móvel de acesso à Internet operacional e capaz de suportar uma demanda de comunicação cada vez maior por parte das aplicações. / The incorporation of computing and communication technologies into modern vehicles is enabling a new generation of connected cars. With the ability to get into a network formation, in the so-called ad hoc networks (VANETs), these vehicles might, in the near future, (i) make the traffic safer for drivers, passengers and pedestrians and/or (ii) promote a more pleasant transportation experience, with greater comfort. It is in this context that emerges the Vehicle CrowdSensing (VCS), a novel and promising paradigm for performing in loco data collection from the vehicles embedded technologies. VCS has proved to be an auspicious scheme for the development and deployment of the Intelligent Transport Systems (ITSs). However, in large urban areas, depending on the phenomenon to be monitored, the VCS applications can generate a colossal data traffic between vehicles and the monitoring center. Considering that all the vehicles information is generally sent to the remote server by using mobile network infrastructures, this massive amount of transmissions generated during the sensing activities can overload them and degrade the Quality of Service (QoS) they offer. This thesis document describes and analyzes a geographic clustering approach that relies on the use of Vehicle-to- Vehicle (V2V) communications to promote the VCS data offloading in cellular networks, in order to minimize the above impacts. The experimental results obtained showed that the use of V2V communications as a complementary data acquisition method was able to considerably reduce the number of transmissions carried out on mobile networks, without the need for deploying new communication infrastructures in the environment, and with a reduced additional delay. The created approach also stands itself as a flexible software platform on which data aggregation techniques can be incorporated, in order to maximize the network resources preservation already provided by the proposal. Considering that we are just entering in the Internet of Things (IoT) and smart cities era, creating data offloading solutions, such as that treated in this research, is considered an essential task to keep the Internet access network operational and able to support the growing demand for mobile communications.

費率組合在電信業市場上行之有年，但長久以來，電信業一直以傳統的語音服務計費模式套用在所有的服務上，其中通話費的部分則是以通話時間作為計費的基礎，甚至連架構在語音服務上的數據傳輸也是如此。新引進的整合封包無線電服務(General Packet Radio Service, GPRS)在技術本質上與語音服務有很大的差異，因此計費方式也必須作一番變革，以傳輸量作為計費的基礎是較佳的選擇。 　由於GPRS屬於寬頻數據網路，再加上無線電資源的有限性，在傳輸品質上的要求也較高，GPRS在設計時就已經規劃了支援「服務品質(Quality of Service，QoS)」。因此，品質因素也應被列入計費的要項之一。 　本文嘗試提出一個GPRS的計費架構，並找出所有的計費因子，使未來經營GPRS的電信業者的行銷人員及計費系統設計人員有一個依循的準則。而在實例中也的確發現本文所提出的GPRS計費架構，可以應用在現實的計費系統上，提供各式各樣的費率組合。 / Pricing Package was used in telecommunications industry for many years. However, the operators always use the conventional voice service packages to cover the data services. The charge for every call is duration based but it cannot be applied for new introduced technology, e.g. GPRS. The technology of GPRS is quite different with the voice services; the charging model will be different as well. A volume-based charging model is an ideal way. 　GPRS is a broadband mobile data service. According to the limitation of wireless resources, the demand of quality of service is high. GPRS was designed to support QoS (Quality of Service) which can be considered as a charging factor. 　This material tried to propose a GPRS charging architecture and find out all of the charging factors. The marketing people and charging system designers of GPRS operators can create their own pricing packages based on this GPRS charging architecture. In the case study, we found that this GPRS charging architecture can be applied to the real world charging systems and provide kinds of pricing packages.

This dissertation is a study on the design and analysis of novel, optimal routing and rate control algorithms in wireless, mobile communication networks. Congestion control and routing algorithms upto now have been designed and optimized for wired or wireless mesh networks. In those networks, optimal algorithms (optimal in the sense that either the throughput is maximized or delay is minimized, or the network operation cost is minimized) can be engineered based on the classic time scale decomposition assumption that the dynamics of the network are either fast enough so that these algorithms essentially see the average or slow enough that any changes can be tracked to allow the algorithms to adapt over time. However, as technological advancements enable integration of ever more mobile nodes into communication networks, any rate control or routing algorithms based, for example, on averaging out the capacity of the wireless mobile link or tracking the instantaneous capacity will perform poorly. The common element in our solution to engineering efficient routing and rate control algorithms for mobile wireless networks is to make the wireless mobile links seem as if they are wired or wireless links to all but few nodes that directly see the mobile links (either the mobiles or nodes that can transmit to or receive from the mobiles) through an appropriate use of queuing structures at these selected nodes. This approach allows us to design end-to-end rate control or routing algorithms for wireless mobile networks so that neither averaging nor instantaneous tracking is necessary, as we have done in the following three networks. A network where we can easily demonstrate the poor performance of a rate control algorithm based on either averaging or tracking is a simple wireless downlink network where a mobile node moves but stays within the coverage cell of a single base station. In such a scenario, the time scale of the variations of the quality of the wireless channel between the mobile user and the base station can be such that the TCP-like congestion control algorithm at the source can not track the variation and is therefore unable to adjust the instantaneous coding rate at which the data stream can be encoded, i.e., the channel variation time scale is matched to the TCP round trip time scale. On the other hand, setting the coding rate for the average case will still result in low throughput due to the high sensitivity of the TCP rate control algorithm to packet loss and the fact that below average channel conditions occur frequently. In this dissertation, we will propose modifications to the TCP congestion control algorithm for this simple wireless mobile downlink network that will improve the throughput without the need for any tracking of the wireless channel. Intermittently connected network (ICN) is another network where the classic assumption of time scale decomposition is no longer relevant. An intermittently connected network is composed of multiple clusters of nodes that are geographically separated. Each cluster is connected wirelessly internally, but inter-cluster communication between two nodes in different clusters must rely on mobile carrier nodes to transport data between clusters. For instance, a mobile would make contact with a cluster and pick up data from that cluster, then move to a different cluster and drop off data into the second cluster. On contact, a large amount of data can be transferred between a cluster and a mobile, but the time duration between successive mobile-cluster contacts can be relatively long. In this network, an inter-cluster rate controller based on instantaneously tracking the mobile-cluster contacts can lead to under utilization of the network resources; if it is based on using long term average achievable rate of the mobile-cluster contacts, this can lead to large buffer requirements within the clusters. We will design and analyze throughput optimal routing and rate control algorithm for ICNs with minimum delay based on a back-pressure algorithm that is neither based on averaging out or tracking the contacts. The last type of network we study is networks with stationary nodes that are far apart from each other that rely on mobile nodes to communicate with each other. Each mobile transport node can be on one of several fixed routes, and these mobiles drop off or pick up data to and from the stationaries that are on that route. Each route has an associated cost that much be paid by the mobiles to be on (a longer route would have larger cost since it would require the mobile to expend more fuel) and stationaries pay different costs to have a packet picked up by the mobiles on different routes. The challenge in this type of network is to design a distributed route selection algorithm for the mobiles and for the stationaries to stabilize the network and minimize the total network operation cost. The sum cost minimization algorithm based on average source rates and mobility movement pattern would require global knowledge of the rates and movement pattern available at all stationaries and mobiles, rendering such algorithm centralized and weak in the presence of network disruptions. Algorithms based on instantaneous contact, on the contrary, would make them impractical as the mobile-stationary contacts are extremely short and infrequent. / text

abstract: The critical infrastructures of the nation are a large and complex network of human, physical and cyber-physical systems. In recent times, it has become increasingly apparent that individual critical infrastructures, such as the power and communication networks, do not operate in isolation, but instead are part of a complex interdependent ecosystem where a failure involving a small set of network entities can trigger a cascading event resulting in the failure of a much larger set of entities through the failure propagation process. Recognizing the need for a deeper understanding of the interdependent relationships between such critical infrastructures, several models have been proposed and analyzed in the last few years. However, most of these models are over-simplified and fail to capture the complex interdependencies that may exist between critical infrastructures. To overcome the limitations of existing models, this dissertation presents a new model -- the Implicative Interdependency Model (IIM) that is able to capture such complex interdependency relations. As the potential for a failure cascade in critical interdependent networks poses several risks that can jeopardize the nation, this dissertation explores relevant research problems in the interdependent power and communication networks using the proposed IIM and lays the foundations for further study using this model. Apart from exploring problems in interdependent critical infrastructures, this dissertation also explores resource allocation techniques for environments enabled with cyber-physical systems. Specifically, the problem of efficient path planning for data collection using mobile cyber-physical systems is explored. Two such environments are considered: a Radio-Frequency IDentification (RFID) environment with mobile “Tags” and “Readers”, and a sensor data collection environment where both the sensors and the data mules (data collectors) are mobile. Finally, from an applied research perspective, this dissertation presents Raptor, an advanced network planning and management tool for mitigating the impact of spatially correlated, or region based faults on infrastructure networks. Raptor consolidates a wide range of studies conducted in the last few years on region based faults, and provides an interface for network planners, designers and operators to use the results of these studies for designing robust and resilient networks in the presence of spatially correlated faults. / Dissertation/Thesis / Doctoral Dissertation Computer Science 2016

Actualmente, los sectores empresariales optimizan sus costos sin perder eficiencia productiva en Data Center Móviles tercer izando el servicio y adquiriendo esta tecnología ya que la infraestructura montada es de utilidad para todo ámbito empresarial. El presente proyecto trata sobre el servicio de implementación de un nuevo Data Center Contingente tipo Container, en adelante CPD, como consecuencia de las necesidades de mejora centro de los procesos de negocio. El diseño y dimensionamiento de los diferentes componentes cumplen los estándares requeridos en el mercado. Los Outdoor Enclosure Electric Shelter Prefabricados en sí y todos los equipamientos eléctricos utilizados en sus sistemas para la protección, control y supervisión, están construidos de acuerdo a las Normas vigentes de ANSI, NEMA, ASTM, IEEE, ISA, OSHA, los cuales además cuentan con Aprobaciones y Certificación de Calidad de Laboratorios como UL, CSA, SEC o Laboratorios de Control de Producción y de Certificación de Calidad equivalentes. Actualmente las empresas vienen usando enlaces propios y dedicados para manejar su información y conexión con su data center principal y de contingencia. En muchos casos desaprovechando la comunicación entre ellos por lo que se requiere la necesidad de ser más efectivos en el uso de sus recursos de comunicación, por lo cual se debe considerar los siguientes aspectos: • Modelamiento de Tráfico. • Seguridad. • Conmutación de enlaces. Currently, the business sectors optimize their costs without losing production efficiency in Mobile Data Center outsourcing service and acquiring this technology because the infrastructure is mounted useful for all business field. This project deals with the service of implementing a new quota type Data Center Container, hereinafter CPD, following needs improvement center business processes. The design and dimensioning of the different components meet the required standards in the market. The Outdoor Enclosure Electric Shelter Prefabricated itself and all electrical equipment used in systems for the protection, control and monitoring are built according to current standards of ANSI, NEMA, ASTM, IEEE, ISA, OSHA, which also have with Approvals and Quality Certification Laboratories as UL, CSA, SEC or Control Laboratories Production and Quality Certification equivalents. Currently companies are using own links and dedicated to manage their information and connection with your main data center and contingency. In many cases missing the communication between them so the need to be more effective in their use of communication resources is required, so you should consider the following: • Traffic Modeling. • Security. • Switching links.

This thesis has taken part as an experimental development within the Learning Ecology through Science with Global Outcomes project. It introduces the area of cross platform mobile application development and provides a possible solution for tackling the current fragmentation of mobile devices and platforms for data collection. During the process, a mobile data collection prototype was designed, implemented and deployed on Android, iOS and Windows Mango by using standards and web standards such as HTML5, CSS3, XForms and JavaScript. The deployed prototype was then tested with users in order to collect the necessary data to help answer the questions that were formulated. The results indicate an ease of use of the prototype in relation to cross platform development and also shows potential benefits such as less code and time. Cross platform development provides a way to counteract the current fragmentation between mobile platforms.

Les réseaux fixes et mobiles font face à une croissance dramatique du trafic de données, qui est principalement due à la distribution de contenus vidéo. Les opérateurs Télécoms envisagent donc de décentraliser la distribution de contenus dans les futures architectures convergées fixe-mobile (FMC). Cette décentralisation, conjointement au déploiement d'un cœur de réseau mobile distribué, sera un élément majeur des futurs réseaux 5G. L'approche SIPTO définie par 3GPP permet déjà le délestage sur le réseau fixe du trafic mobile, et pourra donc être utilisée en 5G. SIPTO s'appuie sur la distribution des passerelles de données (PGW) qui permet ainsi de décharger le cœur du réseau mobile actuel. Cependant, dans certains cas de mobilité des usagers, SIPTO ne supporte pas la continuité de session, quand il est nécessaire de changer de PGW, donc de modifier l'adresse IP du terminal. Cette thèse commence par quantifier le gain apporté par le délestage du trafic mobile en termes de capacité requise pour différentes portions du réseau. Un état de l'art des différentes solutions de délestage du trafic de données mobiles est fourni, démontrant qu'aucune des solutions existantes ne supporte la continuité de service pour les sessions de longue durée. C'est pourquoi, cette thèse propose des solutions pour supporter une mobilité transparente ; ces solutions s'appuient à la fois sur SIPTO et sur le protocole MultiPath TCP (MPTCP). Les protocoles du 3GPP sont inchangés car la continuité est maintenue par les extrémités. Enfin, ces solutions sont appliquées aux différentes implémentations d'architectures FMC envisagées à ce jour. / Fixed and mobile networks are currently experiencing a dramatic growth in terms of data traffic, mainly driven by video content distribution. Telecoms operators are thus considering de-centralizing content distribution architecture for future Fixed and Mobile Converged (FMC) network architectures. This decentralization, together with a distributed mobile EPC, would be used for future 5G networks. Mobile data offloading, in particular SIPTO approaches, already represent a good implementation model for 5G network as it allows the use of distributed IP edges to offload Selected IP traffic off the currently centralized mobile core network. However, in some cases, SIPTO does not support session continuity during users' mobility. This is due to the fact that user's mobility may imply packet gateway (PGW) relocation and thus a modification of the UE's IP address.This PhD thesis first quantifies the gain, in terms of bandwidth demands on various network portions, brought by the generalized use of mobile traffic offloading. A state of art of existing mobile data offloading solutions is presented, showing that none of the existing solutions solve the problem of session continuity for long-lived sessions. This is why, in the context of future FMC mobile network architectures, the PhD thesis proposes solutions to provide seamless mobility for users relying on SIPTO with the help of Multipath TCP (MPTCP). 3GPP standards are not modified, as session continuity is ensured by end-points. Lastly, the proposed solutions are mapped on different architecture options considered for future FMC networks.

Comprehensive information on traffic flow is essential for vehicular emission monitoring and traffic control. However, such information is not observable everywhere and anytime on the road because of high installation costs and malfunctions of stationary sensors. In order to compensate for stationary sensors’ weakness, this thesis analyses an approach for inferring traffic flows from mobile data provided by INRIX, a commercial crowd-sourced traffic dataset with wide spatial coverage and high quality. The idea is to develop Artificial Neural Network (ANN)-based models to automatically extract relations between traffic flow and INRIX measurements, e.g., speed and travel time, from historical data considering temporal and spatial dependencies. We conducted experiments using four weeks of data from INRIX and stationary sensors on two adjacent road segments on the E4 highway in Stockholm. Models are validated via traffic flow estimation based on one week of INRIX data. Compared with the traditional approach that fits the stationary flow-speed relationship based on the multi-regime model, the new approach greatly improves the estimation accuracy. Moreover, the results indicate that the new approach’s models have better resistance to the drift of input variables and can decrease the deterioration of estimation accuracy on the road segment without a stationary sensor. Hence, the new approach may be more appropriate for estimating traffic flows on the nearby road segments of a stationary sensor. The approach provides a highly automated means to build models adaptive to datasets and improves estimation and imputation accuracy. It can also easily integrate new data sources to improve the models. Therefore, it is very suitable to be applied to Intelligent Transport Systems (ITS) for traffic monitor and control in the context of the Internet of Things (IoT) and Big Data. / Information om trafikflödet är nödvändig för övervakning av fordonsutsläpp och trafikstyrning. Trafikflöden kan dock inte observeras överallt och när som helst på vägen på grund av höga installationskostnader och t.ex. funktionsstörningar hos stationära sensorer. För att kompensera för stationära sensorers svagheter analyseras i detta arbete ett tillvägagångssätt för att estimera trafikflöden från mobila data som tillhandahålls av INRIX. Detta kommersiella dataset innehåller restider som kommer från användare av bl.a. färdnavigatorer i fordon och som har en bred rumslig täckning och hög kvalitet. Idén är att utveckla modeller baserade på artificiellt neuronnät för att automatiskt extrahera samband mellan trafikflödesdata och restidsdata från INRIX-mätningarna baserat på historiska data och med hänsyn till tidsmässiga och rumsliga beroenden. Vi utförde experiment med fyra veckors data från INRIX och från stationära sensorer på två intilliggande vägsegment på E4:an i Stockholm. Modellerna valideras med hjälp av estimering av trafikflöde baserat på en veckas INRIX- data. Jämfört med det traditionella tillvägagångssättet som anpassar stationära samband mellan trafikflöde och hastighet baserat på fundamentaldiagram, förbättrar det nya tillvägagångssättet noggrannheten avsevärt. Dessutom visar resultaten att modellerna i den nya metoden bättre hanterar avvikelser i ingående variabler och kan öka noggrannheten på estimatet för vägsegmentet utan stationär sensor. Den nya metoden kan därför vara lämplig för att uppskatta trafikflöden på vägsegment närliggande en stationär sensor. Metodiken ger ett automatiserat sätt att bygga modeller som är anpassade till datamängderna och som förbättrar noggrannheten vid estimering av trafikflöden. Den kan också enkelt integrera nya datakällor. Metodiken är lämplig att tillämpa på tillämpningar inom intelligenta transportsystem för trafikövervakning och trafikstyrning.

La capacité à prévoir les activités humaines a des implications essentielles dans de nombreux aspects des réseaux cellulaires. En particulier, la haute disponibilité de la prédiction de la mobilité peut permettre différents scénarios d'application; une meilleure compréhension de la demande de trafic de données mobiles peut aider à améliorer la conception de solutions pour l'équilibrage de la charge du réseau. Bien que de nombreux chercheurs aient étudié le sujet de la prédiction de la mobilité humaine, il y a eu peu de discussions sur l'anticipation du trafic de données mobiles dans les réseaux cellulaires.Pour comprendre la mobilité humaine, les ensembles de données de téléphones mobiles, consistant en des enregistrements de données de taxation (CDR), constituent un choix pratique d'empreintes humaines. Comme le déploiement du réseau cellulaire est très irrégulier et que les fréquences d'interaction sont généralement faibles, les données CDR sont souvent caractérisées par une parcimonie spatio-temporelle qui, à son tour, peut biaiser les analyses de mobilité basées sur de telles données et provoquer la perte de trajectoires individuelles.Dans cette thèse, nous présentons de nouvelles solutions de reconstruction de trajectoires individuelles et de prédiction de trafic de données mobiles individuelles. Nos contributions abordent les problèmes de (1) surmonter l'incomplétude des informations de mobilité pour l'utilisation des ensembles de données de téléphonie mobile et (2) prédire la future demande de trafic de données mobiles pour le support des applications de gestion de réseau.Premièrement, nous nous concentrons sur la faille de l'information sur la mobilité dans les ensembles de données de téléphones mobiles. Nous rapportons une analyse en profondeur de son effet sur la mesure des caractéristiques de mobilité individuelles et l'exhaustivité des trajectoires individuelles. En particulier, (1) nous fournissons une confirmation des résultats antérieurs concernant les biais dans les mesures de mobilité causées par la rareté temporelle de la CDR; (2) nous évaluons le décalage géographique provoqué par la cartographie des emplacements des utilisateurs vers les tours cellulaires et révélons le biais causé par la rareté spatiale de la CDR; (3) nous fournissons une estimation empirique de l'exhaustivité des données des trajectoires CDR individuelles. (4) nous proposons de nouvelles solutions de complétion CDR pour reconstruire incomplète. Nos solutions tirent parti de la nature des modèles de mouvements humains répétitifs et des techniques d'inférence de données de pointe et surpassent les approches précédentes illustrées par des simulations axées sur les données.Deuxièmement, nous abordons la prédiction des demandes de trafic de données mobiles générées par les abonnés individuels du réseau mobile. Sur la base de trajectoires complétées par nos solutions développées et nos historiques de consommation de données extraites d'un ensemble de données de téléphonie mobile à grande échelle, (1) nous étudions les limites de prévisibilité en mesurant la prévisibilité maximale que tout algorithme peut atteindre. les approches de prédiction du trafic de données mobiles qui utilisent les résultats de l'analyse théorique de la prévisibilité. Notre analyse théorique montre qu'il est théoriquement possible d'anticiper la demande individuelle avec une précision typique de 75% malgré l'hétérogénéité des utilisateurs et avec une précision améliorée de 80% en utilisant la prédiction conjointe avec des informations de mobilité. Notre pratique basée sur des techniques d'apprentissage automatique peut atteindre une précision typique de 65% et avoir un degré d'amélioration de 1% à 5% en considérant les déplacements individuels.En résumé, les contributions mentionnées ci-dessus vont dans le sens de l'utilisation des ensembles de données de téléphonie mobile et de la gestion des opérateurs de réseau et de leurs abonnés. / The understanding of human behaviors is a central question in multi-disciplinary research and has contributed to a wide range of applications. The ability to foresee human activities has essential implications in many aspects of cellular networks. In particular, the high availability of mobility prediction can enable various application scenarios such as location-based recommendation, home automation, and location-related data dissemination; the better understanding of mobile data traffic demand can help to improve the design of solutions for network load balancing, aiming at improving the quality of Internet-based mobile services. Although a large and growing body of literature has investigated the topic of predicting human mobility, there has been little discussion in anticipating mobile data traffic in cellular networks, especially in spatiotemporal view of individuals.For understanding human mobility, mobile phone datasets, consisting of Charging Data Records (CDRs), are a practical choice of human footprints because of the large-scale user populations and the vast diversity of individual movement patterns. The accuracy of mobility information granted by CDR depends on the network infrastructure and the frequency of user communication events. As cellular network deployment is highly irregular and interaction frequencies are typically low, CDR data is often characterized by spatial and temporal sparsity, which, in turn, can bias mobility analyses based on such data and cause the loss of whereabouts in individual trajectories.In this thesis, we present novel solutions of the reconstruction of individual trajectories and the prediction of individual mobile data traffic. Our contributions address the problems of (1) overcoming the incompleteness of mobility information for the use of mobile phone datasets and (2) predicting future mobile data traffic demand for the support of network management applications.First, we focus on the flaw of mobility information in mobile phone datasets. We report on an in-depth analysis of its effect on the measurement of individual mobility features and the completeness of individual trajectories. In particular, (1) we provide a confirmation of previous findings regarding the biases in mobility measurements caused by the temporal sparsity of CDR; (2) we evaluate the geographical shift caused by the mapping of user locations to cell towers and reveal the bias caused by the spatial sparsity of CDR; (3) we provide an empirical estimation of the data completeness of individual CDR-based trajectories. (4) we propose novel solutions of CDR completion to reconstruct incomplete. Our solutions leverage the nature of repetitive human movement patterns and the state-of-the-art data inference techniques and outperform previous approaches shown by data-driven simulations.Second, we address the prediction of mobile data traffic demands generated by individual mobile network subscribers. Building on trajectories completed by our developed solutions and data consumption histories extracted from a large-scale mobile phone dataset, (1) we investigate the limits of predictability by measuring the maximum predictability that any algorithm has potential to achieve and (2) we propose practical mobile data traffic prediction approaches that utilize the findings of the theoretical predictability analysis. Our theoretical analysis shows that it is theoretically possible to anticipate the individual demand with a typical accuracy of 75% despite the heterogeneity of users and with an improved accuracy of 80% using joint prediction with mobility information. Our practical based on machine learning techniques can achieve a typical accuracy of 65% and have a 1%~5% degree of improvement by considering individual whereabouts.In summary, the contributions mentioned above provide a step further towards supporting the use of mobile phone datasets and the management of network operators and their subscribers.