Data analytics for mobile traffic in 5G networks using machine learning techniques

This thesis collects the research works I pursued as Ph.D. candidate at the Universitat Politecnica de Catalunya (UPC). Most of the work has been accomplished at the Mobile Network Department Centre Tecnologic de Telecomunicacions de Catalunya (CTTC). The main topic of my research is the study of mobile network traffic through the analysis of operative networks dataset using machine learning techniques. Understanding first the actual network deployments is fundamental for next-generation network (5G) for improving the performance and Quality of Service (QoS) of the users. The work starts from the collection of a novel type of dataset, using an over-the-air monitoring tool, that allows to extract the control information from the radio-link channel, without harming the users’ identities. The subsequent analysis comprehends a statistical characterization of the traffic and the derivation of prediction models for the network traffic. A wide group of algorithms are implemented and compared, in order to identify the highest performances. Moreover, the thesis addresses a set of applications in the context mobile networks that are prerogatives in the future mobile networks. This includes the detection of urban anomalies, the user classification based on the demanded network services, the design of a proactive wake-up scheme for efficient-energy devices.Esta tesis recoge los trabajos de investigación que realicé como Ph.D. candidato a la Universitat Politecnica de Catalunya (UPC). La mayor parte del trabajo se ha realizado en el Centro Tecnológico de Telecomunicaciones de Catalunya (CTTC) del Departamento de Redes Móviles. El tema principal de mi investigación es el estudio del tráfico de la red móvil a través del análisis del conjunto de datos de redes operativas utilizando técnicas de aprendizaje automático. Comprender primero las implementaciones de red reales es fundamental para la red de próxima generación (5G) para mejorar el rendimiento y la calidad de servicio (QoS) de los usuarios. El trabajo comienza con la recopilación de un nuevo tipo de conjunto de datos, utilizando una herramienta de monitoreo por aire, que permite extraer la información de control del canal de radioenlace, sin dañar las identidades de los usuarios. El análisis posterior comprende una caracterización estadística del tráfico y la derivación de modelos de predicción para el tráfico de red. Se implementa y compara un amplio grupo de algoritmos para identificar los rendimientos más altos. Además, la tesis aborda un conjunto de aplicaciones en el contexto de redes móviles que son prerrogativas en las redes móviles futuras. Esto incluye la detección de anomalías urbanas, la clasificación de usuarios basada en los servicios de red demandados, el diseño de un esquema de activación proactiva para dispositivos de energía eficiente.Postprint (published version

Context-Aware Android Applications through Transportation Mode Detection Techniques

In this paper, we study the problem of how to detect the current transportation mode of the user from the smartphone sensors data, because this issue is considered crucial for the deployment of a multitude of mobility-aware systems, ranging from trace collectors to health monitoring and urban sensing systems. Although some feasibility studies have been performed in the literature, most of the proposed systems rely on the utilization of the GPS and on computational expensive algorithms that do not take into account the limited resources of mobile phones. On the opposite, this paper focuses on the design and implementation of a feasible and efficient detection system that takes into account both the issues of accuracy of classification and of energy consumption. To this purpose, we propose the utilization of embedded sensor data (accelerometer/gyroscope) with a novel meta-classifier based on a cascading technique, and we show that our combined approach can provide similar performance than a GPS-based classifier, but introducing also the possibility to control the computational load based on requested confidence. We describe the implementation of the proposed system into an Android framework that can be leveraged by third-part mobile applications to access context-aware information in a transparent way

Mapping Mountain Landforms and Its Dynamics: Study Cases in Tropical Environments

High mountain areas are critical for water security and natural hazard dynamics, as well as glacier and ecosystem conservation in a warming world. We present a brief account of the methodological steps for geomorphological mapping in mountain areas, including the required scale, the legends, technology, and software. We analyze the best imagery sources and their combination with fieldwork and geographical information systems (GIS), in performing accurate cartography. In addition, we present two case studies in which we apply several methods described previously. Firstly, we carried out a classical and digital geomorphological mapping of Cerro Chirripó (Talamanca Range). Secondly, we studied the Reserva Biológica Alberto Manuel Brenes (Central Volcanic Range), where we used UAVs to map high-resolution fluvial geomorphology. This methodological framework is suitable for future geomorphological surveys in mountain areas worldwide. Moreover, the case studies can give ideas on the application of these approaches to different mountainous environments.UCR::Vicerrectoría de Docencia::Ciencias Sociales::Facultad de Ciencias Sociales::Escuela de Geografí

An Overview on Application of Machine Learning Techniques in Optical Networks

Today's telecommunication networks have become sources of enormous amounts of widely heterogeneous data. This information can be retrieved from network traffic traces, network alarms, signal quality indicators, users' behavioral data, etc. Advanced mathematical tools are required to extract meaningful information from these data and take decisions pertaining to the proper functioning of the networks from the network-generated data. Among these mathematical tools, Machine Learning (ML) is regarded as one of the most promising methodological approaches to perform network-data analysis and enable automated network self-configuration and fault management. The adoption of ML techniques in the field of optical communication networks is motivated by the unprecedented growth of network complexity faced by optical networks in the last few years. Such complexity increase is due to the introduction of a huge number of adjustable and interdependent system parameters (e.g., routing configurations, modulation format, symbol rate, coding schemes, etc.) that are enabled by the usage of coherent transmission/reception technologies, advanced digital signal processing and compensation of nonlinear effects in optical fiber propagation. In this paper we provide an overview of the application of ML to optical communications and networking. We classify and survey relevant literature dealing with the topic, and we also provide an introductory tutorial on ML for researchers and practitioners interested in this field. Although a good number of research papers have recently appeared, the application of ML to optical networks is still in its infancy: to stimulate further work in this area, we conclude the paper proposing new possible research directions

From Social Data Mining to Forecasting Socio-Economic Crisis

Socio-economic data mining has a great potential in terms of gaining a better understanding of problems that our economy and society are facing, such as financial instability, shortages of resources, or conflicts. Without large-scale data mining, progress in these areas seems hard or impossible. Therefore, a suitable, distributed data mining infrastructure and research centers should be built in Europe. It also appears appropriate to build a network of Crisis Observatories. They can be imagined as laboratories devoted to the gathering and processing of enormous volumes of data on both natural systems such as the Earth and its ecosystem, as well as on human techno-socio-economic systems, so as to gain early warnings of impending events. Reality mining provides the chance to adapt more quickly and more accurately to changing situations. Further opportunities arise by individually customized services, which however should be provided in a privacy-respecting way. This requires the development of novel ICT (such as a self- organizing Web), but most likely new legal regulations and suitable institutions as well. As long as such regulations are lacking on a world-wide scale, it is in the public interest that scientists explore what can be done with the huge data available. Big data do have the potential to change or even threaten democratic societies. The same applies to sudden and large-scale failures of ICT systems. Therefore, dealing with data must be done with a large degree of responsibility and care. Self-interests of individuals, companies or institutions have limits, where the public interest is affected, and public interest is not a sufficient justification to violate human rights of individuals. Privacy is a high good, as confidentiality is, and damaging it would have serious side effects for society.Comment: 65 pages, 1 figure, Visioneer White Paper, see http://www.visioneer.ethz.c

Studies of Single-Molecule Dynamics in Microorganisms

Fluorescence microscopy is one of the most extensively used techniques in the life sciences. Considering the non-invasive sample preparation, enabling live-cell compliant imaging, and the specific fluorescence labeling, allowing for a specific visualization of virtually any cellular compound, it is possible to localize even a single molecule in living cells. This makes modern fluorescence microscopy a powerful toolbox. In the recent decades, the development of new, "super-resolution" fluorescence microscopy techniques, which surpass the diffraction limit, revolutionized the field. Single-Molecule Localization Microscopy (SMLM) is a class of super-resolution microscopy methods and it enables resolution of down to tens of nanometers. SMLM methods like Photoactivated Localization Microscopy (PALM), (direct) Stochastic Optical Reconstruction Microscopy ((d)STORM), Ground-State Depletion followed by Individual Molecule Return (GSDIM) and Point Accumulation for Imaging in Nanoscale Topography (PAINT) have allowed to investigate both, the intracellular spatial organization of proteins and to observe their real-time dynamics at the single-molecule level in live cells. The focus of this thesis was the development of novel tools and strategies for live-cell SingleParticle Tracking PALM (sptPALM) imaging and implementing them for biological research. In the first part of this thesis, I describe the development of new Photoconvertible Fluorescent Proteins (pcFPs) which are optimized for sptPALM lowering the phototoxic damage caused by the imaging procedure. Furthermore, we show that we can utilize them together with Photoactivatable Fluorescent Proteins (paFPs) to enable multi-target labeling and read-out in a single color channel, which significantly simplifies the sample preparation and imaging routines as well as data analysis of multi-color PALM imaging of live cells. In parallel to developing new fluorescent proteins, I developed a high throughput data analysis pipeline. I have implemented this pipeline in my second project, described in the second part of this thesis, where I have investigated the protein organization and dynamics of the CRISPR-Cas antiviral defense mechanism of bacteria in vivo at a high spatiotemporal level with the sptPALM approach. I was successful to show the differences in the target search dynamics of the CRISPR effector complexes as well as of single Cas proteins for different target complementarities. I have also first data describing longer-lasting bound-times between effector complex and their potential targets in vivo, for which only in vitro data has been available till today. In summary, this thesis is a significant contribution for both, the advances of current sptPALM imaging methods, as well as for the understanding of the native behavior of CRISPR-Cas systems in vivo

Application of advanced machine learning techniques to early network traffic classification

The fast-paced evolution of the Internet is drawing a complex context which imposes demanding requirements to assure end-to-end Quality of Service. The development of advanced intelligent approaches in networking is envisioning features that include autonomous resource allocation, fast reaction against unexpected network events and so on. Internet Network Traffic Classification constitutes a crucial source of information for Network Management, being decisive in assisting the emerging network control paradigms. Monitoring traffic flowing through network devices support tasks such as: network orchestration, traffic prioritization, network arbitration and cyberthreats detection, amongst others. The traditional traffic classifiers became obsolete owing to the rapid Internet evolution. Port-based classifiers suffer from significant accuracy losses due to port masking, meanwhile Deep Packet Inspection approaches have severe user-privacy limitations. The advent of Machine Learning has propelled the application of advanced algorithms in diverse research areas, and some learning approaches have proved as an interesting alternative to the classic traffic classification approaches. Addressing Network Traffic Classification from a Machine Learning perspective implies numerous challenges demanding research efforts to achieve feasible classifiers. In this dissertation, we endeavor to formulate and solve important research questions in Machine-Learning-based Network Traffic Classification. As a result of numerous experiments, the knowledge provided in this research constitutes an engaging case of study in which network traffic data from two different environments are successfully collected, processed and modeled. Firstly, we approached the Feature Extraction and Selection processes providing our own contributions. A Feature Extractor was designed to create Machine-Learning ready datasets from real traffic data, and a Feature Selection Filter based on fast correlation is proposed and tested in several classification datasets. Then, the original Network Traffic Classification datasets are reduced using our Selection Filter to provide efficient classification models. Many classification models based on CART Decision Trees were analyzed exhibiting excellent outcomes in identifying various Internet applications. The experiments presented in this research comprise a comparison amongst ensemble learning schemes, an exploratory study on Class Imbalance and solutions; and an analysis of IP-header predictors for early traffic classification. This thesis is presented in the form of compendium of JCR-indexed scientific manuscripts and, furthermore, one conference paper is included. In the present work we study a wide number of learning approaches employing the most advance methodology in Machine Learning. As a result, we identify the strengths and weaknesses of these algorithms, providing our own solutions to overcome the observed limitations. Shortly, this thesis proves that Machine Learning offers interesting advanced techniques that open prominent prospects in Internet Network Traffic Classification.Departamento de Teoría de la Señal y Comunicaciones e Ingeniería TelemáticaDoctorado en Tecnologías de la Información y las Telecomunicacione

Estimating poverty maps from aggregated mobile communication networks

Governments and other organisations often rely on data collected by household surveys and censuses to provide estimates of household poverty and identify areas in most need of regeneration and development investment. However, due to the high cost associated with manual data collection and processing, many developing countries conduct such surveys very infrequently, if at all, and only at a coarse level of spatial granularity. Consequently, it becomes difficult for governments and NGOs to determine where and when to intervene. This thesis addresses this problem by examining the feasibility of deriving up to date and high resolution proxy measurements of poverty from an alternative source of data, namely, Call Detail Records (CDRs), which can be used by organisations to help in decision making. Specifically, we contribute the following: 1. A detailed spatial analysis of economic wealth in two sub-Saharan countries, Senegal and Cote d’Ivoire from which we derive two baseline poverty esti- ˆ mators grounded on concrete usage scenarios. 2. We establish a link between communication patterns and wealth through a simulation-based analysis of information diffusion. We further examine the influence of contextual factors, including data quality issues and economic volatility, on the strength of this relationship. 3. An approach to building wealth prediction models based on features of aggregated CDRs. Features include static and simulation based measures of information access, activity based metrics and econometric inspired metrics. We further perform a comparative analysis of the results of several models in relation to the baseline predictors. We conclude that it is possible to produce proxy poverty or wealth indicators from aggregated CDRs that provide a good level of accuracy, particularly where geographical coverage of the mobile phone network is sufficient. The final outcome of this thesis is a method for developing aggregated CDR-based poverty or wealth models that can be readily implemented anywhere in which there is a need for more up to date and/or finer resolution poverty estimates