224 research outputs found

    Timely Classification of Encrypted or ProtocolObfuscated Internet Traffic Using Statistical Methods

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    Internet traffic classification aims to identify the type of application or protocol that generated a particular packet or stream of packets on the network. Through traffic classification, Internet Service Providers (ISPs), governments, and network administrators can access basic functions and several solutions, including network management, advanced network monitoring, network auditing, and anomaly detection. Traffic classification is essential as it ensures the Quality of Service (QoS) of the network, as well as allowing efficient resource planning. With the increase of encrypted or obfuscated protocol traffic on the Internet and multilayer data encapsulation, some classical classification methods have lost interest from the scientific community. The limitations of traditional classification methods based on port numbers and payload inspection to classify encrypted or obfuscated Internet traffic have led to significant research efforts focused on Machine Learning (ML) based classification approaches using statistical features from the transport layer. In an attempt to increase classification performance, Machine Learning strategies have gained interest from the scientific community and have shown promise in the future of traffic classification, specially to recognize encrypted traffic. However, ML approach also has its own limitations, as some of these methods have a high computational resource consumption, which limits their application when classifying large traffic or realtime flows. Limitations of ML application have led to the investigation of alternative approaches, including featurebased procedures and statistical methods. In this sense, statistical analysis methods, such as distances and divergences, have been used to classify traffic in large flows and in realtime. The main objective of statistical distance is to differentiate flows and find a pattern in traffic characteristics through statistical properties, which enable classification. Divergences are functional expressions often related to information theory, which measure the degree of discrepancy between any two distributions. This thesis focuses on proposing a new methodological approach to classify encrypted or obfuscated Internet traffic based on statistical methods that enable the evaluation of network traffic classification performance, including the use of computational resources in terms of CPU and memory. A set of traffic classifiers based on KullbackLeibler and JensenShannon divergences, and Euclidean, Hellinger, Bhattacharyya, and Wootters distances were proposed. The following are the four main contributions to the advancement of scientific knowledge reported in this thesis. First, an extensive literature review on the classification of encrypted and obfuscated Internet traffic was conducted. The results suggest that portbased and payloadbased methods are becoming obsolete due to the increasing use of traffic encryption and multilayer data encapsulation. MLbased methods are also becoming limited due to their computational complexity. As an alternative, Support Vector Machine (SVM), which is also an ML method, and the KolmogorovSmirnov and Chisquared tests can be used as reference for statistical classification. In parallel, the possibility of using statistical methods for Internet traffic classification has emerged in the literature, with the potential of good results in classification without the need of large computational resources. The potential statistical methods are Euclidean Distance, Hellinger Distance, Bhattacharyya Distance, Wootters Distance, as well as KullbackLeibler (KL) and JensenShannon divergences. Second, we present a proposal and implementation of a classifier based on SVM for P2P multimedia traffic, comparing the results with KolmogorovSmirnov (KS) and Chisquare tests. The results suggest that SVM classification with Linear kernel leads to a better classification performance than KS and Chisquare tests, depending on the value assigned to the Self C parameter. The SVM method with Linear kernel and suitable values for the Self C parameter may be a good choice to identify encrypted P2P multimedia traffic on the Internet. Third, we present a proposal and implementation of two classifiers based on KL Divergence and Euclidean Distance, which are compared to SVM with Linear kernel, configured with the standard Self C parameter, showing a reduced ability to classify flows based solely on packet sizes compared to KL and Euclidean Distance methods. KL and Euclidean methods were able to classify all tested applications, particularly streaming and P2P, where for almost all cases they efficiently identified them with high accuracy, with reduced consumption of computational resources. Based on the obtained results, it can be concluded that KL and Euclidean Distance methods are an alternative to SVM, as these statistical approaches can operate in realtime and do not require retraining every time a new type of traffic emerges. Fourth, we present a proposal and implementation of a set of classifiers for encrypted Internet traffic, based on JensenShannon Divergence and Hellinger, Bhattacharyya, and Wootters Distances, with their respective results compared to those obtained with methods based on Euclidean Distance, KL, KS, and ChiSquare. Additionally, we present a comparative qualitative analysis of the tested methods based on Kappa values and Receiver Operating Characteristic (ROC) curves. The results suggest average accuracy values above 90% for all statistical methods, classified as ”almost perfect reliability” in terms of Kappa values, with the exception of KS. This result indicates that these methods are viable options to classify encrypted Internet traffic, especially Hellinger Distance, which showed the best Kappa values compared to other classifiers. We conclude that the considered statistical methods can be accurate and costeffective in terms of computational resource consumption to classify network traffic. Our approach was based on the classification of Internet network traffic, focusing on statistical distances and divergences. We have shown that it is possible to classify and obtain good results with statistical methods, balancing classification performance and the use of computational resources in terms of CPU and memory. The validation of the proposal supports the argument of this thesis, which proposes the implementation of statistical methods as a viable alternative to Internet traffic classification compared to methods based on port numbers, payload inspection, and ML.A classificação de tráfego Internet visa identificar o tipo de aplicação ou protocolo que gerou um determinado pacote ou fluxo de pacotes na rede. Através da classificação de tráfego, Fornecedores de Serviços de Internet (ISP), governos e administradores de rede podem ter acesso às funções básicas e várias soluções, incluindo gestão da rede, monitoramento avançado de rede, auditoria de rede e deteção de anomalias. Classificar o tráfego é essencial, pois assegura a Qualidade de Serviço (QoS) da rede, além de permitir planear com eficiência o uso de recursos. Com o aumento de tráfego cifrado ou protocolo ofuscado na Internet e do encapsulamento de dados multicamadas, alguns métodos clássicos da classificação perderam interesse de investigação da comunidade científica. As limitações dos métodos tradicionais da classificação com base no número da porta e na inspeção de carga útil payload para classificar o tráfego de Internet cifrado ou ofuscado levaram a esforços significativos de investigação com foco em abordagens da classificação baseadas em técnicas de Aprendizagem Automática (ML) usando recursos estatísticos da camada de transporte. Na tentativa de aumentar o desempenho da classificação, as estratégias de Aprendizagem Automática ganharam o interesse da comunidade científica e se mostraram promissoras no futuro da classificação de tráfego, principalmente no reconhecimento de tráfego cifrado. No entanto, a abordagem em ML também têm as suas próprias limitações, pois alguns desses métodos possuem um elevado consumo de recursos computacionais, o que limita a sua aplicação para classificação de grandes fluxos de tráfego ou em tempo real. As limitações no âmbito da aplicação de ML levaram à investigação de abordagens alternativas, incluindo procedimentos baseados em características e métodos estatísticos. Neste sentido, os métodos de análise estatística, tais como distâncias e divergências, têm sido utilizados para classificar tráfego em grandes fluxos e em tempo real. A distância estatística possui como objetivo principal diferenciar os fluxos e permite encontrar um padrão nas características de tráfego através de propriedades estatísticas, que possibilitam a classificação. As divergências são expressões funcionais frequentemente relacionadas com a teoria da informação, que mede o grau de discrepância entre duas distribuições quaisquer. Esta tese focase na proposta de uma nova abordagem metodológica para classificação de tráfego cifrado ou ofuscado da Internet com base em métodos estatísticos que possibilite avaliar o desempenho da classificação de tráfego de rede, incluindo a utilização de recursos computacionais, em termos de CPU e memória. Foi proposto um conjunto de classificadores de tráfego baseados nas Divergências de KullbackLeibler e JensenShannon e Distâncias Euclidiana, Hellinger, Bhattacharyya e Wootters. A seguir resumemse os tese. Primeiro, realizámos uma ampla revisão de literatura sobre classificação de tráfego cifrado e ofuscado de Internet. Os resultados sugerem que os métodos baseados em porta e baseados em carga útil estão se tornando obsoletos em função do crescimento da utilização de cifragem de tráfego e encapsulamento de dados multicamada. O tipo de métodos baseados em ML também está se tornando limitado em função da complexidade computacional. Como alternativa, podese utilizar a Máquina de Vetor de Suporte (SVM), que também é um método de ML, e os testes de KolmogorovSmirnov e Quiquadrado como referência de comparação da classificação estatística. Em paralelo, surgiu na literatura a possibilidade de utilização de métodos estatísticos para classificação de tráfego de Internet, com potencial de bons resultados na classificação sem aporte de grandes recursos computacionais. Os métodos estatísticos potenciais são as Distâncias Euclidiana, Hellinger, Bhattacharyya e Wootters, além das Divergências de Kullback–Leibler (KL) e JensenShannon. Segundo, apresentamos uma proposta e implementação de um classificador baseado na Máquina de Vetor de Suporte (SVM) para o tráfego multimédia P2P (PeertoPeer), comparando os resultados com os testes de KolmogorovSmirnov (KS) e Quiquadrado. Os resultados sugerem que a classificação da SVM com kernel Linear conduz a um melhor desempenho da classificação do que os testes KS e Quiquadrado, dependente do valor atribuído ao parâmetro Self C. O método SVM com kernel Linear e com valores adequados para o parâmetro Self C pode ser uma boa escolha para identificar o tráfego Par a Par (P2P) multimédia cifrado na Internet. Terceiro, apresentamos uma proposta e implementação de dois classificadores baseados na Divergência de KullbackLeibler (KL) e na Distância Euclidiana, sendo comparados com a SVM com kernel Linear, configurado para o parâmestro Self C padrão, apresenta reduzida capacidade de classificar fluxos com base apenas nos tamanhos dos pacotes em relação aos métodos KL e Distância Euclidiana. Os métodos KL e Euclidiano foram capazes de classificar todas as aplicações testadas, destacandose streaming e P2P, onde para quase todos os casos foi eficiente identificálas com alta precisão, com reduzido consumo de recursos computacionais.Com base nos resultados obtidos, podese concluir que os métodos KL e Distância Euclidiana são uma alternativa à SVM, porque essas abordagens estatísticas podem operar em tempo real e não precisam de retreinamento cada vez que surge um novo tipo de tráfego. Quarto, apresentamos uma proposta e implementação de um conjunto de classificadores para o tráfego de Internet cifrado, baseados na Divergência de JensenShannon e nas Distâncias de Hellinger, Bhattacharyya e Wootters, sendo os respetivos resultados comparados com os resultados obtidos com os métodos baseados na Distância Euclidiana, KL, KS e Quiquadrado. Além disso, apresentamos uma análise qualitativa comparativa dos métodos testados com base nos valores de Kappa e Curvas Característica de Operação do Receptor (ROC). Os resultados sugerem valores médios de precisão acima de 90% para todos os métodos estatísticos, classificados como “confiabilidade quase perfeita” em valores de Kappa, com exceçãode KS. Esse resultado indica que esses métodos são opções viáveis para a classificação de tráfego cifrado da Internet, em especial a Distância de Hellinger, que apresentou os melhores resultados do valor de Kappa em comparaçãocom os demais classificadores. Concluise que os métodos estatísticos considerados podem ser precisos e económicos em termos de consumo de recursos computacionais para classificar o tráfego da rede. A nossa abordagem baseouse na classificação de tráfego de rede Internet, focando em distâncias e divergências estatísticas. Nós mostramos que é possível classificar e obter bons resultados com métodos estatísticos, equilibrando desempenho de classificação e uso de recursos computacionais em termos de CPU e memória. A validação da proposta sustenta o argumento desta tese, que propõe a implementação de métodos estatísticos como alternativa viável à classificação de tráfego da Internet em relação aos métodos com base no número da porta, na inspeção de carga útil e de ML.Thesis prepared at Instituto de Telecomunicações Delegação da Covilhã and at the Department of Computer Science of the University of Beira Interior, and submitted to the University of Beira Interior for discussion in public session to obtain the Ph.D. Degree in Computer Science and Engineering. This work has been funded by Portuguese FCT/MCTES through national funds and, when applicable, cofunded by EU funds under the project UIDB/50008/2020, and by operation Centro010145FEDER000019 C4 Centro de Competências em Cloud Computing, cofunded by the European Regional Development Fund (ERDF/FEDER) through the Programa Operacional Regional do Centro (Centro 2020). This work has also been funded by CAPES (Brazilian Federal Agency for Support and Evaluation of Graduate Education) within the Ministry of Education of Brazil under a scholarship supported by the International Cooperation Program CAPES/COFECUB Project 9090134/ 2013 at the University of Beira Interior

    Optimising WLANs Power Saving: Context-Aware Listen Interval

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    Energy is a vital resource in wireless computing systems. Despite the increasing popularity of Wireless Local Area Networks (WLANs), one of the most important outstanding issues remains the power consumption caused by Wireless Network Interface Controller (WNIC). To save this energy and reduce the overall power consumption of wireless devices, a number of power saving approaches have been devised including Static Power Save Mode (SPSM), Adaptive PSM (APSM), and Smart Adaptive PSM (SAPSM). However, the existing literature has highlighted several issues and limitations in regards to their power consumption and performance degradation, warranting the need for further enhancements. This thesis proposes a novel Context-Aware Listen Interval (CALI), in which the wireless network interface, with the aid of a Machine Learning (ML) classification model, sleeps and awakes based on the level of network activity of each application. We focused on the network activity of a single smartphone application while ignoring the network activity of applications running simultaneously. We introduced a context-aware network traffic classification approach based on ML classifiers to classify the network traffic of wireless devices in WLANs. Smartphone applications’ network traffic reflecting a diverse array of network behaviour and interactions were used as contextual inputs for training ML classifiers of output traffic, constructing an ML classification model. A real-world dataset is constructed, based on nine smartphone applications’ network traffic, this is used firstly to evaluate the performance of five ML classifiers using cross-validation, followed by conducting extensive experimentation to assess the generalisation capacity of the selected classifiers on unseen testing data. The experimental results further validated the practical application of the selected ML classifiers and indicated that ML classifiers can be usefully employed for classifying the network traffic of smartphone applications based on different levels of behaviour and interaction. Furthermore, to optimise the sleep and awake cycles of the WNIC in accordance with the smartphone applications’ network activity. Four CALI power saving modes were developed based on the classified output traffic. Hence, the ML classification model classifies the new unseen samples into one of the classes, and the WNIC will be adjusted to operate into one of CALI power saving modes. In addition, the performance of CALI’s power saving modes were evaluated by comparing the levels of energy consumption with existing benchmark power saving approaches using three varied sets of energy parameters. The experimental results show that CALI consumes up to 75% less power when compared to the currently deployed power saving mechanism on the latest generation of smartphones, and up to 14% less energy when compared to SAPSM power saving approach, which also employs an ML classifier

    Jornadas Nacionales de Investigación en Ciberseguridad: actas de las VIII Jornadas Nacionales de Investigación en ciberseguridad: Vigo, 21 a 23 de junio de 2023

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    Jornadas Nacionales de Investigación en Ciberseguridad (8ª. 2023. Vigo)atlanTTicAMTEGA: Axencia para a modernización tecnolóxica de GaliciaINCIBE: Instituto Nacional de Cibersegurida

    A survey of blockchain and artificial intelligence for 6G wireless communications

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    The research on the sixth-generation (6G) wireless communications for the development of future mobile communication networks has been officially launched around the world. 6G networks face multifarious challenges, such as resource-constrained mobile devices, difficult wireless resource management, high complexity of heterogeneous network architectures, explosive computing and storage requirements, privacy and security threats. To address these challenges, deploying blockchain and artificial intelligence (AI) in 6G networks may realize new breakthroughs in advancing network performances in terms of security, privacy, efficiency, cost, and more. In this paper, we provide a detailed survey of existing works on the application of blockchain and AI to 6G wireless communications. More specifically, we start with a brief overview of blockchain and AI. Then, we mainly review the recent advances in the fusion of blockchain and AI, and highlight the inevitable trend of deploying both blockchain and AI in wireless communications. Furthermore, we extensively explore integrating blockchain and AI for wireless communication systems, involving secure services and Internet of Things (IoT) smart applications. Particularly, some of the most talked-about key services based on blockchain and AI are introduced, such as spectrum management, computation allocation, content caching, and security and privacy. Moreover, we also focus on some important IoT smart applications supported by blockchain and AI, covering smart healthcare, smart transportation, smart grid, and unmanned aerial vehicles (UAVs). Moreover, we thoroughly discuss operating frequencies, visions, and requirements from the 6G perspective. We also analyze the open issues and research challenges for the joint deployment of blockchain and AI in 6G wireless communications. Lastly, based on lots of existing meaningful works, this paper aims to provide a comprehensive survey of blockchain and AI in 6G networks. We hope this surve..

    Systematic Approaches for Telemedicine and Data Coordination for COVID-19 in Baja California, Mexico

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    Conference proceedings info: ICICT 2023: 2023 The 6th International Conference on Information and Computer Technologies Raleigh, HI, United States, March 24-26, 2023 Pages 529-542We provide a model for systematic implementation of telemedicine within a large evaluation center for COVID-19 in the area of Baja California, Mexico. Our model is based on human-centric design factors and cross disciplinary collaborations for scalable data-driven enablement of smartphone, cellular, and video Teleconsul-tation technologies to link hospitals, clinics, and emergency medical services for point-of-care assessments of COVID testing, and for subsequent treatment and quar-antine decisions. A multidisciplinary team was rapidly created, in cooperation with different institutions, including: the Autonomous University of Baja California, the Ministry of Health, the Command, Communication and Computer Control Center of the Ministry of the State of Baja California (C4), Colleges of Medicine, and the College of Psychologists. Our objective is to provide information to the public and to evaluate COVID-19 in real time and to track, regional, municipal, and state-wide data in real time that informs supply chains and resource allocation with the anticipation of a surge in COVID-19 cases. RESUMEN Proporcionamos un modelo para la implementación sistemática de la telemedicina dentro de un gran centro de evaluación de COVID-19 en el área de Baja California, México. Nuestro modelo se basa en factores de diseño centrados en el ser humano y colaboraciones interdisciplinarias para la habilitación escalable basada en datos de tecnologías de teleconsulta de teléfonos inteligentes, celulares y video para vincular hospitales, clínicas y servicios médicos de emergencia para evaluaciones de COVID en el punto de atención. pruebas, y para el tratamiento posterior y decisiones de cuarentena. Rápidamente se creó un equipo multidisciplinario, en cooperación con diferentes instituciones, entre ellas: la Universidad Autónoma de Baja California, la Secretaría de Salud, el Centro de Comando, Comunicaciones y Control Informático. de la Secretaría del Estado de Baja California (C4), Facultades de Medicina y Colegio de Psicólogos. Nuestro objetivo es proporcionar información al público y evaluar COVID-19 en tiempo real y rastrear datos regionales, municipales y estatales en tiempo real que informan las cadenas de suministro y la asignación de recursos con la anticipación de un aumento de COVID-19. 19 casos.ICICT 2023: 2023 The 6th International Conference on Information and Computer Technologieshttps://doi.org/10.1007/978-981-99-3236-

    Infrastructure-less D2D Communications through Opportunistic Networks

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    Mención Internacional en el título de doctorIn recent years, we have experienced several social media blackouts, which have shown how much our daily experiences depend on high-quality communication services. Blackouts have occurred because of technical problems, natural disasters, hacker attacks or even due to deliberate censorship actions undertaken by governments. In all cases, the spontaneous reaction of people consisted in finding alternative channels and media so as to reach out to their contacts and partake their experiences. Thus, it has clearly emerged that infrastructured networks—and cellular networks in particular—are well engineered and have been extremely successful so far, although other paradigms should be explored to connect people. The most promising of today’s alternative paradigms is Device-to-Device (D2D) because it allows for building networks almost freely, and because 5G standards are (for the first time) seriously addressing the possibility of using D2D communications. In this dissertation I look at opportunistic D2D networking, possibly operating in an infrastructure-less environment, and I investigate several schemes through modeling and simulation, deriving metrics that characterize their performance. In particular, I consider variations of the Floating Content (FC) paradigm, that was previously proposed in the technical literature. Using FC, it is possible to probabilistically store information over a given restricted local area of interest, by opportunistically spreading it to mobile users while in the area. In more detail, a piece of information which is injected in the area by delivering it to one or more of the mobile users, is opportunistically exchanged among mobile users whenever they come in proximity of one another, progressively reaching most (ideally all) users in the area and thus making the information dwell in the area of interest, like in a sort of distributed storage. While previous works on FC almost exclusively concentrated on the communication component, in this dissertation I look at the storage and computing components of FC, as well as its capability of transferring information from one area of interest to another. I first present background work, including a brief review of my Master Thesis activity, devoted to the design, implementation and validation of a smartphone opportunistic information sharing application. The goal of the app was to collect experimental data that permitted a detailed analysis of the occurring events, and a careful assessment of the performance of opportunistic information sharing services. Through experiments, I showed that many key assumptions commonly adopted in analytical and simulation works do not hold with current technologies. I also showed that the high density of devices and the enforcement of long transmission ranges for links at the edge might counter-intuitively impair performance. The insight obtained during my Master Thesis work was extremely useful to devise smart operating procedures for the opportunistic D2D communications considered in this dissertation. In the core of this dissertation, initially I propose and study a set of schemes to explore and combine different information dissemination paradigms along with real users mobility and predictions focused on the smart diffusion of content over disjoint areas of interest. To analyze the viability of such schemes, I have implemented a Python simulator to evaluate the average availability and lifetime of a piece of information, as well as storage usage and network utilization metrics. Comparing the performance of these predictive schemes with state-of-the-art approaches, results demonstrate the need for smart usage of communication opportunities and storage. The proposed algorithms allow for an important reduction in network activity by decreasing the number of data exchanges by up to 92%, requiring the use of up to 50% less of on-device storage, while guaranteeing the dissemination of information with performance similar to legacy epidemic dissemination protocols. In a second step, I have worked on the analysis of the storage capacity of probabilistic distributed storage systems, developing a simple yet powerful information theoretical analysis based on a mean field model of opportunistic information exchange. I have also extended the previous simulator to compare the numerical results generated by the analytical model to the predictions of realistic simulations under different setups, showing in this way the accuracy of the analytical approach, and characterizing the properties of the system storage capacity. I conclude from analysis and simulated results that when the density of contents seeded in a floating system is larger than the maximum amount which can be sustained by the system in steady state, the mean content availability decreases, and the stored information saturates due to the effects of resource contention. With the presence of static nodes, in a system with infinite host memory and at the mean field limit, there is no upper bound to the amount of injected contents which a floating system can sustain. However, as with no static nodes, by increasing the injected information, the amount of stored information eventually reaches a saturation value which corresponds to the injected information at which the mean amount of time spent exchanging content during a contact is equal to the mean duration of a contact. As a final step of my dissertation, I have also explored by simulation the computing and learning capabilities of an infrastructure-less opportunistic communication, storage and computing system, considering an environment that hosts a distributed Machine Learning (ML) paradigm that uses observations collected in the area over which the FC system operates to infer properties of the area. Results show that the ML system can operate in two regimes, depending on the load of the FC scheme. At low FC load, the ML system in each node operates on observations collected by all users and opportunistically shared among nodes. At high FC load, especially when the data to be opportunistically exchanged becomes too large to be transmitted during the average contact time between nodes, the ML system can only exploit the observations endogenous to each user, which are much less numerous. As a result, I conclude that such setups are adequate to support general instances of distributed ML algorithms with continuous learning, only under the condition of low to medium loads of the FC system. While the load of the FC system induces a sort of phase transition on the ML system performance, the effect of computing load is more progressive. When the computing capacity is not sufficient to train all observations, some will be skipped, and performance progressively declines. In summary, with respect to traditional studies of the FC opportunistic information diffusion paradigm, which only look at the communication component over one area of interest, I have considered three types of extensions by looking at the performance of FC: over several disjoint areas of interest; in terms of information storage capacity; in terms of computing capacity that supports distributed learning. The three topics are treated respectively in Chapters 3 to 5.This work has been supported by IMDEA Networks InstitutePrograma de Doctorado en Ingeniería Telemática por la Universidad Carlos III de MadridPresidente: Claudio Ettori Casetti.- Secretario: Antonio de la Oliva Delgado.- Vocal: Christoph Somme

    IoT and Sensor Networks in Industry and Society

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    The exponential progress of Information and Communication Technology (ICT) is one of the main elements that fueled the acceleration of the globalization pace. Internet of Things (IoT), Artificial Intelligence (AI) and big data analytics are some of the key players of the digital transformation that is affecting every aspect of human's daily life, from environmental monitoring to healthcare systems, from production processes to social interactions. In less than 20 years, people's everyday life has been revolutionized, and concepts such as Smart Home, Smart Grid and Smart City have become familiar also to non-technical users. The integration of embedded systems, ubiquitous Internet access, and Machine-to-Machine (M2M) communications have paved the way for paradigms such as IoT and Cyber Physical Systems (CPS) to be also introduced in high-requirement environments such as those related to industrial processes, under the forms of Industrial Internet of Things (IIoT or I2oT) and Cyber-Physical Production Systems (CPPS). As a consequence, in 2011 the German High-Tech Strategy 2020 Action Plan for Germany first envisioned the concept of Industry 4.0, which is rapidly reshaping traditional industrial processes. The term refers to the promise to be the fourth industrial revolution. Indeed, the first industrial revolution was triggered by water and steam power. Electricity and assembly lines enabled mass production in the second industrial revolution. In the third industrial revolution, the introduction of control automation and Programmable Logic Controllers (PLCs) gave a boost to factory production. As opposed to the previous revolutions, Industry 4.0 takes advantage of Internet access, M2M communications, and deep learning not only to improve production efficiency but also to enable the so-called mass customization, i.e. the mass production of personalized products by means of modularized product design and flexible processes. Less than five years later, in January 2016, the Japanese 5th Science and Technology Basic Plan took a further step by introducing the concept of Super Smart Society or Society 5.0. According to this vision, in the upcoming future, scientific and technological innovation will guide our society into the next social revolution after the hunter-gatherer, agrarian, industrial, and information eras, which respectively represented the previous social revolutions. Society 5.0 is a human-centered society that fosters the simultaneous achievement of economic, environmental and social objectives, to ensure a high quality of life to all citizens. This information-enabled revolution aims to tackle today’s major challenges such as an ageing population, social inequalities, depopulation and constraints related to energy and the environment. Accordingly, the citizens will be experiencing impressive transformations into every aspect of their daily lives. This book offers an insight into the key technologies that are going to shape the future of industry and society. It is subdivided into five parts: the I Part presents a horizontal view of the main enabling technologies, whereas the II-V Parts offer a vertical perspective on four different environments. The I Part, dedicated to IoT and Sensor Network architectures, encompasses three Chapters. In Chapter 1, Peruzzi and Pozzebon analyse the literature on the subject of energy harvesting solutions for IoT monitoring systems and architectures based on Low-Power Wireless Area Networks (LPWAN). The Chapter does not limit the discussion to Long Range Wise Area Network (LoRaWAN), SigFox and Narrowband-IoT (NB-IoT) communication protocols, but it also includes other relevant solutions such as DASH7 and Long Term Evolution MAchine Type Communication (LTE-M). In Chapter 2, Hussein et al. discuss the development of an Internet of Things message protocol that supports multi-topic messaging. The Chapter further presents the implementation of a platform, which integrates the proposed communication protocol, based on Real Time Operating System. In Chapter 3, Li et al. investigate the heterogeneous task scheduling problem for data-intensive scenarios, to reduce the global task execution time, and consequently reducing data centers' energy consumption. The proposed approach aims to maximize the efficiency by comparing the cost between remote task execution and data migration. The II Part is dedicated to Industry 4.0, and includes two Chapters. In Chapter 4, Grecuccio et al. propose a solution to integrate IoT devices by leveraging a blockchain-enabled gateway based on Ethereum, so that they do not need to rely on centralized intermediaries and third-party services. As it is better explained in the paper, where the performance is evaluated in a food-chain traceability application, this solution is particularly beneficial in Industry 4.0 domains. Chapter 5, by De Fazio et al., addresses the issue of safety in workplaces by presenting a smart garment that integrates several low-power sensors to monitor environmental and biophysical parameters. This enables the detection of dangerous situations, so as to prevent or at least reduce the consequences of workers accidents. The III Part is made of two Chapters based on the topic of Smart Buildings. In Chapter 6, Petroșanu et al. review the literature about recent developments in the smart building sector, related to the use of supervised and unsupervised machine learning models of sensory data. The Chapter poses particular attention on enhanced sensing, energy efficiency, and optimal building management. In Chapter 7, Oh examines how much the education of prosumers about their energy consumption habits affects power consumption reduction and encourages energy conservation, sustainable living, and behavioral change, in residential environments. In this Chapter, energy consumption monitoring is made possible thanks to the use of smart plugs. Smart Transport is the subject of the IV Part, including three Chapters. In Chapter 8, Roveri et al. propose an approach that leverages the small world theory to control swarms of vehicles connected through Vehicle-to-Vehicle (V2V) communication protocols. Indeed, considering a queue dominated by short-range car-following dynamics, the Chapter demonstrates that safety and security are increased by the introduction of a few selected random long-range communications. In Chapter 9, Nitti et al. present a real time system to observe and analyze public transport passengers' mobility by tracking them throughout their journey on public transport vehicles. The system is based on the detection of the active Wi-Fi interfaces, through the analysis of Wi-Fi probe requests. In Chapter 10, Miler et al. discuss the development of a tool for the analysis and comparison of efficiency indicated by the integrated IT systems in the operational activities undertaken by Road Transport Enterprises (RTEs). The authors of this Chapter further provide a holistic evaluation of efficiency of telematics systems in RTE operational management. The book ends with the two Chapters of the V Part on Smart Environmental Monitoring. In Chapter 11, He et al. propose a Sea Surface Temperature Prediction (SSTP) model based on time-series similarity measure, multiple pattern learning and parameter optimization. In this strategy, the optimal parameters are determined by means of an improved Particle Swarm Optimization method. In Chapter 12, Tsipis et al. present a low-cost, WSN-based IoT system that seamlessly embeds a three-layered cloud/fog computing architecture, suitable for facilitating smart agricultural applications, especially those related to wildfire monitoring. We wish to thank all the authors that contributed to this book for their efforts. We express our gratitude to all reviewers for the volunteering support and precious feedback during the review process. We hope that this book provides valuable information and spurs meaningful discussion among researchers, engineers, businesspeople, and other experts about the role of new technologies into industry and society

    Estudo do IPFS como protocolo de distribuição de conteúdos em redes veiculares

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    Over the last few years, vehicular ad-hoc networks (VANETs) have been the focus of great progress due to the interest in autonomous vehicles and in distributing content not only between vehicles, but also to the Cloud. Performing a download/upload to/from a vehicle typically requires the existence of a cellular connection, but the costs associated with mobile data transfers in hundreds or thousands of vehicles quickly become prohibitive. A VANET allows the costs to be several orders of magnitude lower - while keeping the same large volumes of data - because it is strongly based in the communication between vehicles (nodes of the network) and the infrastructure. The InterPlanetary File System (IPFS) is a protocol for storing and distributing content, where information is addressed by its content, instead of its location. It was created in 2014 and it seeks to connect all computing devices with the same system of files, comparable to a BitTorrent swarm exchanging Git objects. It has been tested and deployed in wired networks, but never in an environment where nodes have intermittent connectivity, such as a VANET. This work focuses on understanding IPFS, how/if it can be applied to the vehicular network context, and comparing it with other content distribution protocols. In this dissertation, IPFS has been tested in a small and controlled network to understand its working applicability to VANETs. Issues such as neighbor discoverability times and poor hashing performance have been addressed. To compare IPFS with other protocols (such as Veniam’s proprietary solution or BitTorrent) in a relevant way and in a large scale, an emulation platform was created. The tests in this emulator were performed in different times of the day, with a variable number of files and file sizes. Emulated results show that IPFS is on par with Veniam’s custom V2V protocol built specifically for V2V, and greatly outperforms BitTorrent regarding neighbor discoverability and data transfers. An analysis of IPFS’ performance in a real scenario was also conducted, using a subset of STCP’s vehicular network in Oporto, with the support of Veniam. Results from these tests show that IPFS can be used as a content dissemination protocol, showing it is up to the challenge provided by a constantly changing network topology, and achieving throughputs up to 2.8 MB/s, values similar or in some cases even better than Veniam’s proprietary solution.Nos últimos anos, as redes veiculares (VANETs) têm sido o foco de grandes avanços devido ao interesse em veículos autónomos e em distribuir conteúdos, não só entre veículos mas também para a "nuvem" (Cloud). Tipicamente, fazer um download/upload de/para um veículo exige a utilização de uma ligação celular (SIM), mas os custos associados a fazer transferências com dados móveis em centenas ou milhares de veículos rapidamente se tornam proibitivos. Uma VANET permite que estes custos sejam consideravelmente inferiores - mantendo o mesmo volume de dados - pois é fortemente baseada na comunicação entre veículos (nós da rede) e a infraestrutura. O InterPlanetary File System (IPFS - "sistema de ficheiros interplanetário") é um protocolo de armazenamento e distribuição de conteúdos, onde a informação é endereçada pelo conteúdo, em vez da sua localização. Foi criado em 2014 e tem como objetivo ligar todos os dispositivos de computação num só sistema de ficheiros, comparável a um swarm BitTorrent a trocar objetos Git. Já foi testado e usado em redes com fios, mas nunca num ambiente onde os nós têm conetividade intermitente, tal como numa VANET. Este trabalho tem como foco perceber o IPFS, como/se pode ser aplicado ao contexto de rede veicular e compará-lo a outros protocolos de distribuição de conteúdos. Numa primeira fase o IPFS foi testado numa pequena rede controlada, de forma a perceber a sua aplicabilidade às VANETs, e resolver os seus primeiros problemas como os tempos elevados de descoberta de vizinhos e o fraco desempenho de hashing. De modo a poder comparar o IPFS com outros protocolos (tais como a solução proprietária da Veniam ou o BitTorrent) de forma relevante e em grande escala, foi criada uma plataforma de emulação. Os testes neste emulador foram efetuados usando registos de mobilidade e conetividade veicular de alturas diferentes de um dia, com um número variável de ficheiros e tamanhos de ficheiros. Os resultados destes testes mostram que o IPFS está a par do protocolo V2V da Veniam (desenvolvido especificamente para V2V e VANETs), e que o IPFS é significativamente melhor que o BitTorrent no que toca ao tempo de descoberta de vizinhos e transferência de informação. Uma análise do desempenho do IPFS em cenário real também foi efetuada, usando um pequeno conjunto de nós da rede veicular da STCP no Porto, com o apoio da Veniam. Os resultados destes testes demonstram que o IPFS pode ser usado como protocolo de disseminação de conteúdos numa VANET, mostrando-se adequado a uma topologia constantemente sob alteração, e alcançando débitos até 2.8 MB/s, valores parecidos ou nalguns casos superiores aos do protocolo proprietário da Veniam.Mestrado em Engenharia de Computadores e Telemátic

    Net Neutrality

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    This book is available as open access through the Bloomsbury Open Access programme and is available on www.bloomsburycollections.com. Chris Marsden maneuvers through the hype articulated by Netwrok Neutrality advocates and opponents. He offers a clear-headed analysis of the high stakes in this debate about the Internet's future, and fearlessly refutes the misinformation and misconceptions that about' Professor Rob Freiden, Penn State University Net Neutrality is a very heated and contested policy principle regarding access for content providers to the Internet end-user, and potential discrimination in that access where the end-user's ISP (or another ISP) blocks that access in part or whole. The suggestion has been that the problem can be resolved by either introducing greater competition, or closely policing conditions for vertically integrated service, such as VOIP. However, that is not the whole story, and ISPs as a whole have incentives to discriminate between content for matters such as network management of spam, to secure and maintain customer experience at current levels, and for economic benefit from new Quality of Service standards. This includes offering a ‘priority lane' on the network for premium content types such as video and voice service. The author considers market developments and policy responses in Europe and the United States, draws conclusions and proposes regulatory recommendations

    Unmanned Aerial Vehicle (UAV)-Enabled Wireless Communications and Networking

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    The emerging massive density of human-held and machine-type nodes implies larger traffic deviatiolns in the future than we are facing today. In the future, the network will be characterized by a high degree of flexibility, allowing it to adapt smoothly, autonomously, and efficiently to the quickly changing traffic demands both in time and space. This flexibility cannot be achieved when the network’s infrastructure remains static. To this end, the topic of UAVs (unmanned aerial vehicles) have enabled wireless communications, and networking has received increased attention. As mentioned above, the network must serve a massive density of nodes that can be either human-held (user devices) or machine-type nodes (sensors). If we wish to properly serve these nodes and optimize their data, a proper wireless connection is fundamental. This can be achieved by using UAV-enabled communication and networks. This Special Issue addresses the many existing issues that still exist to allow UAV-enabled wireless communications and networking to be properly rolled out
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