Mobile edge computing-based data-driven deep learning framework for anomaly detection

5G is anticipated to embed an artificial intelligence (AI)-empowerment to adroitly plan, optimize and manage the highly complex network by leveraging data generated at different positions of the network architecture. Outages and situation leading to congestion in a cell pose severe hazard for the network. High false alarms and inadequate accuracy are the major limitations of modern approaches for the anomaly—outage and sudden hype in traffic activity that may result in congestion—detection in mobile cellular networks. This indicates wasting limited resources that ultimately leads to an elevated operational expenditure (OPEX) and also interrupting quality of service (QoS) and quality of experience (QoE). Motivated by the outstanding success of deep learning (DL) technology, our study applies it for detection of the above-mentioned anomalies and also supports mobile edge computing (MEC) paradigm in which core network (CN)’s computations are divided across the cellular infrastructure among different MEC servers (co-located with base stations), to relief the CN. Each server monitors user activities of multiple cells and utilizes $L$ -layer feedforward deep neural network (DNN) fueled by real call detail record (CDR) dataset for anomaly detection. Our framework achieved 98.8% accuracy with 0.44% false positive rate (FPR)—notable improvements that surmount the deficiencies of the old studies. The numerical results explicate the usefulness and dominance of our proposed detector

IoT Data Processing for Smart City and Semantic Web Applications

The world has been experiencing rapid urbanization over the last few decades, putting a strain on existing city infrastructure such as waste management, water supply management, public transport and electricity consumption. We are also seeing increasing pollution levels in cities threatening the environment, natural resources and health conditions. However, we must realize that the real growth lies in urbanization as it provides many opportunities to individuals for better employment, healthcare and better education. However, it is imperative to limit the ill effects of rapid urbanization through integrated action plans to enable the development of growing cities. This gave rise to the concept of a smart city in which all available information associated with a city will be utilized systematically for better city management. The proposed system architecture is divided in subsystems and is discussed in individual chapters. The first chapter introduces and gives overview to the reader of the complete system architecture. The second chapter discusses the data monitoring system and data lake system based on the oneM2M standards. DMS employs oneM2M as a middleware layer to achieve interoperability, and DLS uses a multi-tenant architecture with multiple logical databases, enabling efficient and reliable data management. The third chapter discusses energy monitoring and electric vehicle charging systems developed to illustrate the applicability of the oneM2M standards. The fourth chapter discusses the Data Exchange System based on the Indian Urban Data Exchange framework. DES uses IUDX standard data schema and open APIs to avoid data silos and enable secure data sharing. The fifth chapter discusses the 5D-IoT framework that provides uniform data quality assessment of sensor data with meaningful data descriptions

PROFILING - CONCEPTS AND APPLICATIONS

Profiling is an approach to put a label or a set of labels on a subject, considering the characteristics of this subject. The New Oxford American Dictionary defines profiling as: “recording and analysis of a person’s psychological and behavioral characteristics, so as to assess or predict his/her capabilities in a certain sphere or to assist in identifying a particular subgroup of people”. This research extends this definition towards things demonstrating that many methods used for profiling of people may be applied for a different type of subjects, namely things. The goal of this research concerns proposing methods for discovery of profiles of users and things with application of Data Science methods. The profiles are utilized in vertical and 2 horizontal scenarios and concern such domains as smart grid and telecommunication (vertical scenarios), and support provided both for the needs of authorization and personalization (horizontal usage).:The thesis consists of eight chapters including an introduction and a summary. First chapter describes motivation for work that was carried out for the last 8 years together with discussion on its importance both for research and business practice. The motivation for this work is much broader and emerges also from business importance of profiling and personalization. The introduction summarizes major research directions, provides research questions, goals and supplementary objectives addressed in the thesis. Research methodology is also described, showing impact of methodological aspects on the work undertaken. Chapter 2 provides introduction to the notion of profiling. The definition of profiling is introduced. Here, also a relation of a user profile to an identity is discussed. The papers included in this chapter show not only how broadly a profile may be understood, but also how a profile may be constructed considering different data sources. Profiling methods are introduced in Chapter 3. This chapter refers to the notion of a profile developed using the BFI-44 personality test and outcomes of a survey related to color preferences of people with a specific personality. Moreover, insights into profiling of relations between people are provided, with a focus on quality of a relation emerging from contacts between two entities. Chapters from 4 to 7 present different scenarios that benefit from application of profiling methods. Chapter 4 starts with introducing the notion of a public utility company that in the thesis is discussed using examples from smart grid and telecommunication. Then, in chapter 4 follows a description of research results regarding profiling for the smart grid, focusing on a profile of a prosumer and forecasting demand and production of the electric energy in the smart grid what can be influenced e.g. by weather or profiles of appliances. Chapter 5 presents application of profiling techniques in the field of telecommunication. Besides presenting profiling methods based on telecommunication data, in particular on Call Detail Records, also scenarios and issues related to privacy and trust are addressed. Chapter 6 and Chapter 7 target at horizontal applications of profiling that may be of benefit for multiple domains. Chapter 6 concerns profiling for authentication using un-typical data sources such as Call Detail Records or data from a mobile phone describing the user behavior. Besides proposing methods, also limitations are discussed. In addition, as a side research effect a methodology for evaluation of authentication methods is proposed. Chapter 7 concerns personalization and consists of two diverse parts. Firstly, behavioral profiles to change interface and behavior of the system are proposed and applied. The performance of solutions personalizing content either locally or on the server is studied. Then, profiles of customers of shopping centers are created based on paths identified using Call Detail Records. The analysis demonstrates that the data that is collected for one purpose, may significantly influence other business scenarios. Chapter 8 summarizes the research results achieved by the author of this document. It presents contribution over state of the art as well as some insights into the future work planned

Security-centric analysis and performance investigation of IEEE 802.16 WiMAX

fi=vertaisarvioitu|en=peerReviewed

An Approach to Guide Users Towards Less Revealing Internet Browsers

When browsing the Internet, HTTP headers enable both clients and servers send extra data in their requests or responses such as the User-Agent string. This string contains information related to the sender’s device, browser, and operating system. Previous research has shown that there are numerous privacy and security risks result from exposing sensitive information in the User-Agent string. For example, it enables device and browser fingerprinting and user tracking and identification. Our large analysis of thousands of User-Agent strings shows that browsers differ tremendously in the amount of information they include in their User-Agent strings. As such, our work aims at guiding users towards using less exposing browsers. In doing so, we propose to assign an exposure score to browsers based on the information they expose and vulnerability records. Thus, our contribution in this work is as follows: first, provide a full implementation that is ready to be deployed and used by users. Second, conduct a user study to identify the effectiveness and limitations of our proposed approach. Our implementation is based on using more than 52 thousand unique browsers. Our performance and validation analysis show that our solution is accurate and efficient. The source code and data set are publicly available and the solution has been deployed

Machine Learning-based Orchestration Solutions for Future Slicing-Enabled Mobile Networks

The fifth generation mobile networks (5G) will incorporate novel technologies such as network programmability and virtualization enabled by Software-Defined Networking (SDN) and Network Function Virtualization (NFV) paradigms, which have recently attracted major interest from both academic and industrial stakeholders. Building on these concepts, Network Slicing raised as the main driver of a novel business model where mobile operators may open, i.e., “slice”, their infrastructure to new business players and offer independent, isolated and self-contained sets of network functions and physical/virtual resources tailored to specific services requirements. While Network Slicing has the potential to increase the revenue sources of service providers, it involves a number of technical challenges that must be carefully addressed. End-to-end (E2E) network slices encompass time and spectrum resources in the radio access network (RAN), transport resources on the fronthauling/backhauling links, and computing and storage resources at core and edge data centers. Additionally, the vertical service requirements’ heterogeneity (e.g., high throughput, low latency, high reliability) exacerbates the need for novel orchestration solutions able to manage end-to-end network slice resources across different domains, while satisfying stringent service level agreements and specific traffic requirements. An end-to-end network slicing orchestration solution shall i) admit network slice requests such that the overall system revenues are maximized, ii) provide the required resources across different network domains to fulfill the Service Level Agreements (SLAs) iii) dynamically adapt the resource allocation based on the real-time traffic load, endusers’ mobility and instantaneous wireless channel statistics. Certainly, a mobile network represents a fast-changing scenario characterized by complex spatio-temporal relationship connecting end-users’ traffic demand with social activities and economy. Legacy models that aim at providing dynamic resource allocation based on traditional traffic demand forecasting techniques fail to capture these important aspects. To close this gap, machine learning-aided solutions are quickly arising as promising technologies to sustain, in a scalable manner, the set of operations required by the network slicing context. How to implement such resource allocation schemes among slices, while trying to make the most efficient use of the networking resources composing the mobile infrastructure, are key problems underlying the network slicing paradigm, which will be addressed in this thesis

Fatias de rede fim-a-fim : da extração de perfis de funções de rede a SLAs granulares

Orientador: Christian Rodolfo Esteve RothenbergTese (doutorado) - Universidade Estadual de Campinas, Faculdade de Engenharia Elétrica e de ComputaçãoResumo: Nos últimos dez anos, processos de softwarização de redes vêm sendo continuamente diversi- ficados e gradativamente incorporados em produção, principalmente através dos paradigmas de Redes Definidas por Software (ex.: regras de fluxos de rede programáveis) e Virtualização de Funções de Rede (ex.: orquestração de funções virtualizadas de rede). Embasado neste processo o conceito de network slice surge como forma de definição de caminhos de rede fim- a-fim programáveis, possivelmente sobre infrastruturas compartilhadas, contendo requisitos estritos de desempenho e dedicado a um modelo particular de negócios. Esta tese investiga a hipótese de que a desagregação de métricas de desempenho de funções virtualizadas de rede impactam e compõe critérios de alocação de network slices (i.e., diversas opções de utiliza- ção de recursos), os quais quando realizados devem ter seu gerenciamento de ciclo de vida implementado de forma transparente em correspondência ao seu caso de negócios de comu- nicação fim-a-fim. A verificação de tal assertiva se dá em três aspectos: entender os graus de liberdade nos quais métricas de desempenho de funções virtualizadas de rede podem ser expressas; métodos de racionalização da alocação de recursos por network slices e seus re- spectivos critérios; e formas transparentes de rastrear e gerenciar recursos de rede fim-a-fim entre múltiplos domínios administrativos. Para atingir estes objetivos, diversas contribuições são realizadas por esta tese, dentre elas: a construção de uma plataforma para automatização de metodologias de testes de desempenho de funções virtualizadas de redes; a elaboração de uma metodologia para análises de alocações de recursos de network slices baseada em um algoritmo classificador de aprendizado de máquinas e outro algoritmo de análise multi- critério; e a construção de um protótipo utilizando blockchain para a realização de contratos inteligentes envolvendo acordos de serviços entre domínios administrativos de rede. Por meio de experimentos e análises sugerimos que: métricas de desempenho de funções virtualizadas de rede dependem da alocação de recursos, configurações internas e estímulo de tráfego de testes; network slices podem ter suas alocações de recursos coerentemente classificadas por diferentes critérios; e acordos entre domínios administrativos podem ser realizados de forma transparente e em variadas formas de granularidade por meio de contratos inteligentes uti- lizando blockchain. Ao final deste trabalho, com base em uma ampla discussão as perguntas de pesquisa associadas à hipótese são respondidas, de forma que a avaliação da hipótese proposta seja realizada perante uma ampla visão das contribuições e trabalhos futuros desta teseAbstract: In the last ten years, network softwarisation processes have been continuously diversified and gradually incorporated into production, mainly through the paradigms of Software Defined Networks (e.g., programmable network flow rules) and Network Functions Virtualization (e.g., orchestration of virtualized network functions). Based on this process, the concept of network slice emerges as a way of defining end-to-end network programmable paths, possibly over shared network infrastructures, requiring strict performance metrics associated to a par- ticular business case. This thesis investigate the hypothesis that the disaggregation of network function performance metrics impacts and composes a network slice footprint incurring in di- verse slicing feature options, which when realized should have their Service Level Agreement (SLA) life cycle management transparently implemented in correspondence to their fulfilling end-to-end communication business case. The validation of such assertive takes place in three aspects: the degrees of freedom by which performance of virtualized network functions can be expressed; the methods of rationalizing the footprint of network slices; and transparent ways to track and manage network assets among multiple administrative domains. In order to achieve such goals, a series of contributions were achieved by this thesis, among them: the construction of a platform for automating methodologies for performance testing of virtual- ized network functions; an elaboration of a methodology for the analysis of footprint features of network slices based on a machine learning classifier algorithm and a multi-criteria analysis algorithm; and the construction of a prototype using blockchain to carry out smart contracts involving service level agreements between administrative systems. Through experiments and analysis we suggest that: performance metrics of virtualized network functions depend on the allocation of resources, internal configurations and test traffic stimulus; network slices can have their resource allocations consistently analyzed/classified by different criteria; and agree- ments between administrative domains can be performed transparently and in various forms of granularity through blockchain smart contracts. At the end of his thesis, through a wide discussion we answer all the research questions associated to the investigated hypothesis in such way its evaluation is performed in face of wide view of the contributions and future work of this thesisDoutoradoEngenharia de ComputaçãoDoutor em Engenharia ElétricaFUNCAM

View on 5G Architecture: Version 2.0

The 5G Architecture Working Group as part of the 5GPPP Initiative is looking at capturing novel trends and key technological enablers for the realization of the 5G architecture. It also targets at presenting in a harmonized way the architectural concepts developed in various projects and initiatives (not limited to 5GPPP projects only) so as to provide a consolidated view on the technical directions for the architecture design in the 5G era. The first version of the white paper was released in July 2016, which captured novel trends and key technological enablers for the realization of the 5G architecture vision along with harmonized architectural concepts from 5GPPP Phase 1 projects and initiatives. Capitalizing on the architectural vision and framework set by the first version of the white paper, this Version 2.0 of the white paper presents the latest findings and analyses with a particular focus on the concept evaluations, and accordingly it presents the consolidated overall architecture design

Analyzing Granger causality in climate data with time series classification methods

Attribution studies in climate science aim for scientifically ascertaining the influence of climatic variations on natural or anthropogenic factors. Many of those studies adopt the concept of Granger causality to infer statistical cause-effect relationships, while utilizing traditional autoregressive models. In this article, we investigate the potential of state-of-the-art time series classification techniques to enhance causal inference in climate science. We conduct a comparative experimental study of different types of algorithms on a large test suite that comprises a unique collection of datasets from the area of climate-vegetation dynamics. The results indicate that specialized time series classification methods are able to improve existing inference procedures. Substantial differences are observed among the methods that were tested