A Long-range Context-aware Platform Design For Rural Monitoring With IoT In Precision Agriculture

The Internet of Things (IoT) applications has been developing greatly in recent years to solve communication problems, especially in rural areas. Within the IoT, the context-awareness paradigm, especially in precision agricultural practices, has come to a state of the planning of production time. As smart cities approach, the smart environment approach also increases its place in IoT applications and has dominated research in recent years in literature. In this study, soil and environmental information were collected in 17 km diameter in rural area with developed Long Range (LoRa) based context-aware platform. With the developed sensor and actuator control unit, soil moisture at 5 cm and 30 cm depth and soil surface temperature information were collected and the communication performance was investigated. During the study, the performance measurements of the developed Serial Peripheral Interface (SPI) enabled Long Range Wide Area Network (LoRaWAN) gateway were also performed

A holistic review of cybersecurity and reliability perspectives in smart airports

Advances in the Internet of Things (IoT) and aviation sector have resulted in the emergence of smart airports. Services and systems powered by the IoT enable smart airports to have enhanced robustness, efficiency and control, governed by real-time monitoring and analytics. Smart sensors control the environmental conditions inside the airport, automate passenger-related actions and support airport security. However, these augmentations and automation introduce security threats to network systems of smart airports. Cyber-attackers demonstrated the susceptibility of IoT systems and networks to Advanced Persistent Threats (APT), due to hardware constraints, software flaws or IoT misconfigurations. With the increasing complexity of attacks, it is imperative to safeguard IoT networks of smart airports and ensure reliability of services, as cyber-attacks can have tremendous consequences such as disrupting networks, cancelling travel, or stealing sensitive information. There is a need to adopt and develop new Artificial Intelligence (AI)-enabled cyber-defence techniques for smart airports, which will address the challenges brought about by the incorporation of IoT systems to the airport business processes, and the constantly evolving nature of contemporary cyber-attacks. In this study, we present a holistic review of existing smart airport applications and services enabled by IoT sensors and systems. Additionally, we investigate several types of cyber defence tools including AI and data mining techniques, and analyse their strengths and weaknesses in the context of smart airports. Furthermore, we provide a classification of smart airport sub-systems based on their purpose and criticality and address cyber threats that can affect the security of smart airport\u27s networks

Ambient intelligence in buildings : design and development of an interoperable Internet of Things platform

During many years, people and governments have been warned about the increasing levels of pollution and greenhouse gases (GHG) emissions that are endangering our lives on this planet. The Information and Communication Technology sector, usually known as the ICT sector, responsible for the computerization of the society, has been pinpointed as one of the most important sectors contributing to such a problem. Many efforts, however, have been put to shift the trend towards the utilization of renewable resources, such as wind or solar power. Even though governments have agreed to follow this path and avoid the usage of non-renewable energies, it is not enough. Although the ICT sector might seem an added problem due to the number of connected devices, technology improvements and hardware optimization enable new ways of fighting against global warming and GHG emissions. The aforementioned computerization has forced companies to evolve their work into a computer-assisted one. Due to this, companies are now forced to establish their main headquarters inside buildings for work coordination, connection and management. Due to this, buildings are becoming one of the most important issues regarding energy consumption. In order to cope with such problem, the Internet of Things (IoT) offers new paradigms and alternatives for leading the change. IoT is commonly defined as the network of physical and virtual objects that are capable of collecting surrounding data and exchanging it between them or through the Internet. Thanks to these networks, it is possible to monitor any thinkable metric inside buildings, and, then, utilize this information to build efficient automated systems, commonly known as Building Energy Management Systems (BEMS), capable of extracting conclusions on how to optimally and efficiently manage the resources of the building. ICT companies have foreseen this market opportunity that, paired with the appearance of smaller, efficient and more durable sensors, allows the development of efficient IoT systems. However, the lack of agreement and standardization creates chaos inside IoT, and the horizontal connectivity between such systems is still a challenge. Moreover, the vast amount of data to process requires the utilization of Big Data techniques to guarantee close to real-time responses. This thesis initially presents a standard Cloud-based IoT architecture that tries to cope with the aforementioned problems by employing a Cloud middleware that obfuscates the underlying hardware architecture and permits the aggregation of data from multiple heterogeneous sources. Also, sensor information is exposed to any third-party client after authentication. The utilization of automated IoT systems for managing building resources requires high reliability, resilience, and availability. The loss of sensor data is not permitted due to the negative consequences it might have, such as disruptive resource management. For this, it is mandatory to grant backup options to sensor networks in order to guarantee correct functioning in case of partial network disconnections. Additionally, the placement of the sensors inside the building must guarantee minimal energy consumption while fulfilling sensing requirements. Finally, a building resource management use case is presented by means of a simulation tool. The tool draws on occupants' probabilistic models and environmental condition models for actuating upon building elements to ensure optimal and efficient functioning. Occupants' comfort is also taken into consideration and the trade-off between the two metrics is studied. All the presented work is meant to deliver insights and tools for current and future IoT system implementations by setting the basis for standardization agreements yet to happen.Durant molts anys, s'ha alertat a la població i als governs sobre l'increment en els nivells de pol·lució i d'emissió de gasos d'efecte hivernacle, que estan posant en perill la nostra vida a la Terra. El sector de les Tecnologies de la Informació i Comunicació, normalment conegut com les TIC, responsable de la informatització de la societat, ha estat senyalat com un dels sectors més importants encarregat d'agreujar tal problema. Però, molt esforç s'està posant per revertir aquesta situació mitjançant l'ús de recursos renovables, com l'energia eòlica o solar. Tot i que els governs han acordat seguir dit camí i evitar l'ús d'energia no renovable tant com sigui possible, no és suficient per erradicar el problema. Encara que el sector de les TIC pugui semblar un problema afegit donada la gran quantitat i l'increment de dispositius connectats, les millores en tecnologia i en hardware estan habilitant noves maneres de lluitar contra l'escalfament global i l'emissió de gasos d'efecte hivernacle. La informatització, anteriorment mencionada, ha forçat a les empreses a evolucionar el seu model de negoci cap a un més enfocat a la utilització de xarxes d'ordinadors per gestionar els seus recursos. Per això, dites companyies s'estan veient forçades a establir les seves seus centrals dintre d'edificis, per tenir un major control sobre la coordinació, connexió i maneig dels seus recursos. Això està provocant un augment en el consum energètic dels edificis, que s'estan convertint en un dels principals problemes. Per poder fer front al problema, la Internet de les Coses o Internet of Things (IoT) ofereix nous paradigmes i alternatives per liderar el canvi. IoT es defineix com la xarxa d'objectes físics i virtuals, capaços de recol·lectar la informació per construir sistemes automatitzats, coneguts com a Sistemes de Gestió Energètica per Edificis, capaços d'extreure conclusions sobre com utilitzar de manera eficient i òptima els recursos de l'edifici. Companyies pertanyents a les TIC han previst aquesta oportunitat de mercat que, en sincronia amb l'aparició de sensors més petits, eficients i duradors, permeten el desenvolupament de sistemes IoT eficients. Però, la falta d'acord en quant a l'estandardització de dits sistemes està creant un escenari caòtic, ja que s'està fent impossible la connectivitat horitzontal entre dits sistemes. A més, la gran quantitat de dades a processar requereix la utilització de tècniques de Big Data per poder garantir respostes en temps acceptables. Aquesta tesi presenta, inicialment, una arquitectura IoT estàndard basada en la Neu, que tracta de fer front als problemes anteriorment presentats mitjançant l'ús d'un middleware allotjat a la Neu que ofusca l'arquitectura hardware subjacent i permet l'agregació de la informació originada des de múltiples fonts heterogènies. A més, la informació dels sensors s'exposa perquè qualsevol client de tercers pugui consultar-la, després d'haver-se autenticat. La utilització de sistemes IoT automatitzats per gestionar els recursos dels edificis requereix un alt nivell de fiabilitat, resistència i disponibilitat. La perduda d'informació no està permesa degut a les conseqüències negatives que podría suposar, com una mala presa de decisions. Per això, és obligatori atorgar opcions de backup a les xarxes de sensors per garantir un correcte funcionament inclús quan es produeixen desconnexions parcials de la xarxa. Addicionalment, la col·locació dels sensors dintre de l'edifici ha de garantir un consum energètic mínim dintre de les restriccions de desplegament imposades. Finalment, presentem un cas d'ús d'un Sistema de Gestió Energètica per Edificis mitjançant una eina de simulació. Dita eina utilitza com informació d'entrada models probabilístics sobre les accions dels ocupants i models sobre la condició ambiental per actuar sobre els elements de l'edifici i garantir un funcionament òptim i eficient. A més, el confort dels ocupants també es considera com mètrica a optimitzar. Donada la impossibilitat d’optimitzar les dues mètriques de manera conjunta, aquesta tesi també presenta un estudi sobre el trade-off que existeix entre elles. Tot el treball presentat està pensat per atorgar idees i eines pels sistemes IoT actuals i futurs, i assentar les bases per l’estandardització que encara està per arribar.Durante muchos años, se ha alertado a la población y a los gobiernos acerca del incremento en los niveles de polución y de emisión de gases de efecto invernadero, que están poniendo en peligro nuestra vida en la Tierra. El sector de las Tecnologías de la Información y Comunicación, normalmente conocido como las TIC, responsable de la informatización de la sociedad, ha sido señalada como uno de los sectores más importantes encargado de agravar tal problema. Sin embargo, mucho esfuerzo se está poniendo para revertir esta situación mediante el uso de recursos renovables, como la energía eólica o solar. A pesar de que los gobiernos han acordado seguir dicho camino y evitar el uso de energía no renovable tanto como sea posible, no es suficiente para erradicar el problema. Aunque el sector de las TIC pueda parecer un problema añadido dada la gran cantidad y el incremento de dispositivos conectados, las mejoras en tecnología y en hardware están habilitando nuevas maneras de luchar contra el calentamiento global y la emisión de gases de efecto invernadero. Durante las últimas décadas, compañías del sector público y privado conscientes del problema han centrado sus esfuerzos en la creación de soluciones orientadas a la eficiencia energética tanto a nivel de hardware como de software. Las nuevas redes troncales están siendo creadas con dispositivos eficientes y los proveedores de servicios de Internet tienden a crear sistemas conscientes de la energía para su optimización dentro de su dominio. Siguiendo esta tendencia, cualquier nuevo sistema creado y añadido a la red debe garantizar un cierto nivel de conciencia y un manejo óptimo de los recursos que utiliza. La informatización, anteriormente mencionada, ha forzado a las empresas a evolucionar su modelo de negocio hacia uno más enfocado en la utilización de redes de ordenadores para gestionar sus recursos. Por eso, dichas compañías se están viendo forzadas a establecer sus sedes centrales dentro de edificios, para tener un mayor control sobre la coordinación, conexión y manejo de sus recursos. Esto está provocando un aumento en el consumo energético de los edificios, que se están convirtiendo en uno de los principales problemas. Para poder hacer frente al problema, el Internet de las Cosas o Internet of Things (IoT) ofrece nuevos paradigmas y alternativas para liderar el cambio. IoT se define como la red de objetos físicos y virtuales, capaces de recolectar la información del entorno e intercambiarla entre los propios objetos o a través de Internet. Gracias a estas redes, es posible monitorizar cualquier métrica que podamos imaginar dentro de un edificio, y, después, utilizar dicha información para construir sistemas automatizados, conocidos como Sistemas de Gestión Energética para Edificios, capaces de extraer conclusiones sobre cómo utilizar de manera eficiente y óptima los recursos del edificio. Compañías pertenecientes a las TIC han previsto esta oportunidad de mercado que, en sincronía con la aparición de sensores más pequeños, eficientes y duraderos, permite el desarrollo de sistemas IoT eficientes. Sin embargo, la falta de acuerdo en cuanto a la estandarización de dichos sistemas está creando un escenario caótico, ya que se hace imposible la conectividad horizontal entre dichos sistemas. Además, la gran cantidad de datos a procesar requiere la utilización de técnicas de Big Data para poder garantizar respuestas en tiempos aceptables. Esta tesis presenta, inicialmente, una arquitectura IoT estándar basada en la Nube que trata de hacer frente a los problemas anteriormente presentados mediante el uso de un middleware alojado en la Nube que ofusca la arquitectura hardware subyacente y permite la agregación de la información originada des de múltiples fuentes heterogéneas. Además, la información de los sensores se expone para que cualquier cliente de terceros pueda consultarla, después de haberse autenticado. La utilización de sistemas IoT automatizados para manejar los recursos de los edificios requiere un alto nivel de fiabilidad, resistencia y disponibilidad. La pérdida de información no está permitida debido a las consecuencias negativas que podría suponer, como una mala toma de decisiones. Por eso, es obligatorio otorgar opciones de backup a las redes de sensores para garantizar su correcto funcionamiento incluso cuando se producen desconexiones parciales de la red. Adicionalmente, la colocación de los sensores dentro del edificio debe garantizar un consumo energético mínimo dentro de las restricciones de despliegue impuestas. En esta tesis, mejoramos el problema de colocación de los sensores para redes heterogéneas de sensores inalámbricos añadiendo restricciones de clustering o agrupamiento, para asegurar que cada tipo de sensor es capaz de obtener su métrica correspondiente, y restricciones de protección mediante la habilitación de rutas de transmisión secundarias. En cuanto a grandes redes homogéneas de sensores inalámbricos, esta tesis estudia aumentar su resiliencia mediante la identificación de los sensores más críticos. Finalmente, presentamos un caso de uso de un Sistema de Gestión Energética para Edificios mediante una herramienta de simulación. Dicha herramienta utiliza como información de entrada modelos probabilísticos sobre las acciones de los ocupantes y modelos sobre la condición ambiental para actuar sobre los elementos del edificio y garantizar un funcionamiento óptimo y eficiente. Además, el comfort de los ocupantes también se considera como métrica a optimizar. Dada la imposibilidad de optimizar las dos métricas de manera conjunta, esta tesis también presenta un estudio sobre el trade-off que existe entre ellas. Todo el trabajo presentado está pensado para otorgar ideas y herramientas para los sistemas IoT actuales y futuros, y asentar las bases para la estandarización que todavía está por llegar.Postprint (published version

Internet of Things Architectures for Enhanced Living Environments

Ambient Assisted Living (AAL) is an emerging multidisciplinary research area that aims to create an ecosystem of different types of sensors, computers, mobile devices, wireless networks, and software applications for enhanced living environments and occupational health. There are several challenges in the development and implementation of an effective AAL system, such as system architecture, human-computer interaction, ergonomics, usability, and accessibility. There are also social and ethical challenges, such as acceptance by seniors and the privacy and confidentiality that must be a requirement of AAL devices. It is also essential to ensure that technology does not replace human care and is used as a relevant complement. The Internet of Things (IoT) is a paradigm where objects are connected to the Internet and support sensing capabilities. IoT devices should be ubiquitous, recognize the context, and support intelligence capabilities closely related to AAL. Technological advances allow defining new advanced tools and platforms for real-time health monitoring and decision making in the treatment of various diseases. IoT is a suitable approach to building healthcare systems, and it provides a suitable platform for ubiquitous health services, using, for example, portable sensors to carry data to servers and smartphones for communication. Despite the potential of the IoT paradigm and technologies for healthcare systems, several challenges to be overcome still exist. The direction and impact of IoT in the economy are not clearly defined, and there are barriers to the immediate and ubiquitous adoption of IoT products, services, and solutions. Several sources of pollutants have a high impact on indoor living environments. Consequently, indoor air quality is recognized as a fundamental variable to be controlled for enhanced health and well-being. It is critical to note that typically most people occupy more than 90% of their time inside buildings, and poor indoor air quality negatively affects performance and productivity. Research initiatives are required to address air quality issues to adopt legislation and real-time inspection mechanisms to improve public health, not only to monitor public places, schools, and hospitals but also to increase the rigor of building rules. Therefore, it is necessary to use real-time monitoring systems for correct analysis of indoor air quality to ensure a healthy environment in at least public spaces. In most cases, simple interventions provided by homeowners can produce substantial positive impacts on indoor air quality, such as avoiding indoor smoking and the correct use of natural ventilation. An indoor air quality monitoring system helps the detection and improvement of air quality conditions. Local and distributed assessment of chemical concentrations is significant for safety (e.g., detection of gas leaks and monitoring of pollutants) as well as to control heating, ventilation, and HVAC systems to improve energy efficiency. Real-time indoor air quality monitoring provides reliable data for the correct control of building automation systems and should be assumed as a decision support platform on planning interventions for enhanced living environments. However, the monitoring systems currently available are expensive and only allow the collection of random samples that are not provided with time information. Most solutions on the market only allow data consulting limited to device memory and require procedures for downloading and manipulating data with specific software. In this way, the development of innovative environmental monitoring systems based on ubiquitous technologies that allow real-time analysis becomes essential. This thesis resulted in the design and development of IoT architectures using modular and scalable structures for air quality monitoring based on data collected from cost-effective sensors for enhanced living environments. The proposed architectures address several concepts, including acquisition, processing, storage, analysis, and visualization of data. These systems incorporate an alert management Framework that notifies the user in real-time in poor indoor air quality scenarios. The software Framework supports multiple alert methods, such as push notifications, SMS, and e-mail. The real-time notification system offers several advantages when the goal is to achieve effective changes for enhanced living environments. On the one hand, notification messages promote behavioral changes. These alerts allow the building manager to identify air quality problems and plan interventions to avoid unhealthy air quality scenarios. The proposed architectures incorporate mobile computing technologies such as mobile applications that provide ubiquitous air quality data consulting methods s. Also, the data is stored and can be shared with medical teams to support the diagnosis. The state-of-the-art analysis has resulted in a review article on technologies, applications, challenges, opportunities, open-source IoT platforms, and operating systems. This review was significant to define the IoT-based Framework for indoor air quality supervision. The research leads to the development and design of cost-effective solutions based on open-source technologies that support Wi-Fi communication and incorporate several advantages such as modularity, scalability, and easy installation. The results obtained are auspicious, representing a significant contribution to enhanced living environments and occupational health. Particulate matter (PM) is a complex mixture of solid and liquid particles of organic and inorganic substances suspended in the air. Moreover, it is considered the pollutant that affects more people. The most damaging particles to health are ≤PM10 (diameter 10 microns or less), which can penetrate and lodge deep within the lungs, contributing to the risk of developing cardiovascular and respiratory diseases as well as lung cancer. Taking into account the adverse health effects of PM exposure, an IoT architecture for automatic PM monitoring was proposed. The proposed architecture is a PM real-time monitoring system and a decision-making tool. The solution consists of a hardware prototype for data acquisition and a Web Framework developed in .NET for data consulting. This system is based on open-source and technologies, with several advantages compared to existing systems, such as modularity, scalability, low-cost and easy installation. The data is stored in a database developed in SQL SERVER using .NET Web services. The results show the ability of the system to analyze the indoor air quality in real-time and the potential of the Web Framework for the planning of interventions to ensure safe, healthy, and comfortable conditions. Associations of high concentrations of carbon dioxide (CO2) with low productivity at work and increased health problems are well documented. There is also a clear correlation between high levels of CO2 and high concentrations of pollutants in indoor air. There are sufficient reasons to monitor CO2 and provide real-time notifications to improve occupational health and provide a safe and healthy indoor living environment. Taking into account the significant influence of CO2 for enhanced living environments, a real-time IoT architecture for CO2 monitoring was proposed. CO2 was selected because it is easy to measure and is produced in quantity (by people and combustion equipment). It can be used as an indicator of other pollutants and, therefore, of air quality in general. The solution consists of a hardware prototype for data acquisition environment, a Web software, and a smartphone application for data consulting. The proposed architecture is based on open-source technologies, and the data is stored in a SQL SERVER database. The mobile Framework allows the user not only to consult the latest data collected but also to receive real-time notifications in poor indoor air quality scenarios, and to configure the alerts threshold levels. The results show that the mobile application not only provides easy access to real-time air quality data, but also allows the user to maintain parameter history and provide a history of changes. Consequently, this system allows the user to analyze in a precise and detailed manner the behavior of air quality. Finally, an air quality monitoring solution was implemented, consisting of a hardware prototype that incorporates only the MICS-6814 sensor as the detection unit. This system monitors various air quality parameters such as NH3 (ammonia), CO (carbon monoxide), NO2 (nitrogen dioxide), C3H8 (propane), C4H10 (butane), CH4 (methane), H2 (hydrogen) and C2H5OH (ethanol). The monitoring of the concentrations of these pollutants is essential to provide enhanced living environments. This solution is based on Cloud, and the collected data is sent to the ThingSpeak platform. The proposed Framework combines sensitivity, flexibility, and measurement accuracy in real-time, allowing a significant evolution of current air quality controls. The results show that this system provides easy, intuitive, and fast access to air quality data as well as relevant notifications in poor air quality situations to provide real-time intervention and improve occupational health. These data can be accessed by physicians to support diagnoses and correlate the symptoms and health problems of patients with the environment in which they live. As future work, the results reported in this thesis can be considered as a starting point for the development of a secure system sharing data with health professionals in order to serve as decision support in diagnosis.Ambient Assisted Living (AAL) é uma área de investigação multidisciplinar emergente que visa a construção de um ecossistema de diferentes tipos de sensores, microcontroladores, dispositivos móveis, redes sem fios e aplicações de software para melhorar os ambientes de vida e a saúde ocupacional. Existem muitos desafios no desenvolvimento e na implementação de um sistema AAL, como a arquitetura do sistema, interação humano-computador, ergonomia, usabilidade e acessibilidade. Existem também problemas sociais e éticos, como a aceitação por parte dos utilizadores mais vulneráveis e a privacidade e confidencialidade, que devem ser uma exigência de todos os dispositivos AAL. De facto, também é essencial assegurar que a tecnologia não substitua o cuidado humano e seja usada como um complemento essencial. A Internet das Coisas (IoT) é um paradigma em que os objetos estão conectados à Internet e suportam recursos sensoriais. Tendencialmente, os dispositivos IoT devem ser omnipresentes, reconhecer o contexto e ativar os recursos de inteligência ambiente intimamente relacionados ao AAL. Os avanços tecnológicos permitem definir novas ferramentas avançadas e plataformas para monitorização de saúde em tempo real e tomada de decisão no tratamento de várias doenças. A IoT é uma abordagem adequada para construir sistemas de saúde sendo que oferece uma plataforma para serviços de saúde ubíquos, usando, por exemplo, sensores portáteis para recolha e transmissão de dados e smartphones para comunicação. Apesar do potencial do paradigma e tecnologias IoT para o desenvolvimento de sistemas de saúde, muitos desafios continuam ainda por ser resolvidos. A direção e o impacto das soluções IoT na economia não está claramente definido existindo, portanto, barreiras à adoção imediata de produtos, serviços e soluções de IoT. Os ambientes de vida são caracterizados por diversas fontes de poluentes. Consequentemente, a qualidade do ar interior é reconhecida como uma variável fundamental a ser controlada de forma a melhorar a saúde e o bem-estar. É importante referir que tipicamente a maioria das pessoas ocupam mais de 90% do seu tempo no interior de edifícios e que a má qualidade do ar interior afeta negativamente o desempenho e produtividade. É necessário que as equipas de investigação continuem a abordar os problemas de qualidade do ar visando a adoção de legislação e mecanismos de inspeção que atuem em tempo real para a melhoraria da saúde e qualidade de vida, tanto em locais públicos como escolas e hospitais e residências particulares de forma a aumentar o rigor das regras de construção de edifícios. Para tal, é necessário utilizar mecanismos de monitorização em tempo real de forma a possibilitar a análise correta da qualidade do ambiente interior para garantir ambientes de vida saudáveis. Na maioria dos casos, intervenções simples que podem ser executadas pelos proprietários ou ocupantes da residência podem produzir impactos positivos substanciais na qualidade do ar interior, como evitar fumar em ambientes fechados e o uso correto de ventilação natural. Um sistema de monitorização e avaliação da qualidade do ar interior ajuda na deteção e na melhoria das condições ambiente. A avaliação local e distribuída das concentrações químicas é significativa para a segurança (por exemplo, deteção de fugas de gás e supervisão dos poluentes) bem como para controlar o aquecimento, ventilação, e sistemas de ar condicionado (HVAC) visando a melhoria da eficiência energética. A monitorização em tempo real da qualidade do ar interior fornece dados fiáveis para o correto controlo de sistemas de automação de edifícios e deve ser assumida com uma plataforma de apoio à decisão no que se refere ao planeamento de intervenções para ambientes de vida melhorados. No entanto, os sistemas de monitorização atualmente disponíveis são de alto custo e apenas permitem a recolha de amostras aleatórias que não são providas de informação temporal. A maioria das soluções disponíveis no mercado permite apenas a acesso ao histórico de dados que é limitado à memória do dispositivo e exige procedimentos de download e manipulação de dados com software proprietário. Desta forma, o desenvolvimento de sistemas inovadores de monitorização ambiente baseados em tecnologias ubíquas e computação móvel que permitam a análise em tempo real torna-se essencial. A Tese resultou na definição e no desenvolvimento de arquiteturas para monitorização da qualidade do ar baseadas em IoT. Os métodos propostos são de baixo custo e recorrem a estruturas modulares e escaláveis para proporcionar ambientes de vida melhorados. As arquiteturas propostas abordam vários conceitos, incluindo aquisição, processamento, armazenamento, análise e visualização de dados. Os métodos propostos incorporam Frameworks de gestão de alertas que notificam o utilizador em tempo real e de forma ubíqua quando a qualidade do ar interior é deficiente. A estrutura de software suporta vários métodos de notificação, como notificações remotas para smartphone, SMS (Short Message Service) e email. O método usado para o envio de notificações em tempo real oferece várias vantagens quando o objetivo é alcançar mudanças efetivas para ambientes de vida melhorados. Por um lado, as mensagens de notificação promovem mudanças de comportamento. De facto, estes alertas permitem que o gestor do edifício e os ocupantes reconheçam padrões da qualidade do ar e permitem também um correto planeamento de intervenções de forma evitar situações em que a qualidade do ar é deficiente. Por outro lado, o sistema proposto incorpora tecnologias de computação móvel, como aplicações móveis, que fornecem acesso omnipresente aos dados de qualidade do ar e, consequentemente, fornecem soluções completas para análise de dados. Além disso, os dados são armazenados e podem ser partilhados com equipas médicas para ajudar no diagnóstico. A análise do estado da arte resultou na elaboração de um artigo de revisão sobre as tecnologias, aplicações, desafios, plataformas e sistemas operativos que envolvem a criação de arquiteturas IoT. Esta revisão foi um trabalho fundamental na definição das arquiteturas propostas baseado em IoT para a supervisão da qualidade do ar interior. Esta pesquisa conduz a um desenvolvimento de arquiteturas IoT de baixo custo com base em tecnologias de código aberto que operam como um sistema Wi-Fi e suportam várias vantagens, como modularidade, escalabilidade e facilidade de instalação. Os resultados obtidos são muito promissores, representando uma contribuição significativa para ambientes de vida melhorados e saúde ocupacional. O material particulado (PM) é uma mistura complexa de partículas sólidas e líquidas de substâncias orgânicas e inorgânicas suspensas no ar e é considerado o poluente que afeta mais pessoas. As partículas mais prejudiciais à saúde são as ≤PM10 (diâmetro de 10 micrómetros ou menos), que podem penetrar e fixarem-se dentro dos pulmões, contribuindo para o risco de desenvolver doenças cardiovasculares e respiratórias, bem como de cancro do pulmão. Tendo em consideração os efeitos negativos para a saúde da exposição ao PM foi desenvolvido numa primeira fase uma arquitetura IoT para monitorização automática dos níveis de PM. Esta arquitetura é um sistema que permite monitorização de PM em tempo real e uma ferramenta de apoio à tomada de decisão. A solução é composta por um protótipo de hardware para aquisição de dados e um portal Web desenvolvido em .NET para consulta de dados. Este sistema é baseado em tecnologias de código aberto com várias vantagens em comparação aos sistemas existentes, como modularidade, escalabilidade, baixo custo e fácil instalação. Os dados são armazenados numa base de dados desenvolvida em SQL SERVER e são enviados com recurso a serviços Web. Os resultados mostram a capacidade do sistema de analisar em tempo real a qualidade do ar interior e o potencial da Framework Web para o planeamento de intervenções com o objetivo de garantir condições seguras, saudáveis e confortáveis. Associações de altas concentrações de dióxido de carbono (CO2) com défice de produtividade no trabalho e aumento de problemas de saúde encontram-se bem documentadas. Existe também uma correlação evidente entre altos níveis de CO2 e altas concentrações de poluentes no ar interior. Tendo em conta a influência significativa do CO2 para a construção de ambientes de vida melhorados desenvolveu-se uma solução de monitorização em tempo real de CO2 com base na arquitetura de IoT. A arquitetura proposta permite também o envio de notificações em tempo real para melhorar a saúde ocupacional e proporcionar um ambiente de vida interior seguro e saudável. O CO2 foi selecionado, pois é fácil de medir e é produzido em quantidade (por pessoas e equipamentos de combustão). Assim, pode ser usado como um indicador de outros poluentes e, portanto, da qualidade do ar em geral. O método proposto é composto por um protótipo de hardware para aquisição de dados, um software Web e uma aplicação smartphone para consulta de dados. Esta arquitetura é baseada em tecnologias de código aberto e os dados recolhidos são armazenados numa base de dados SQL SERVER. A Framework móvel permite não só consultar em tempo real os últimos dados recolhidos, receber notificações com o objetivo de avisar o utilizador quando a qualidade do ar está deficiente, mas também para configurar alertas. Os resultados mostram que a Framework móvel fornece não apenas acesso fácil aos dados da qualidade do ar em tempo real, mas também permite ao utilizador manter o histórico de parâmetros. Assim este sistema permite ao utilizador analisar de maneira precisa e detalhada o comportamento da qualidade do ar interior. Por último, é proposta uma arquitetura para monitorização de vários parâmetros da qualidade do ar, como NH3 (amoníaco), CO (monóxido de carbono), NO2 (dióxido de azoto), C3H8 (propano), C4H10 (butano), CH4 (metano), H2 (hidrogénio) e C2H5OH (etanol). Esta arquitetura é composta por um protótipo de hardware que incorpora unicamente o sensor MICS-6814 como unidade de deteção. O controlo das concentrações destes poluentes é extremamente relevante para proporcionar ambientes de vida melhorados. Esta solução tem base na Cloud sendo que os dados recolhidos são enviados para a plataforma ThingSpeak. Esta Framework combina sensibilidade, flexibilidade e precisão de medição em tempo real, permitindo uma evolução significativa dos atuais sistemas de monitorização da qualidade do ar. Os resultados mostram que este sistema fornece acesso fácil, intuitivo e rápido aos dados de qualidade do ar bem como notificações essenciais em situações de qualidade do ar deficiente de forma a planear intervenções em tempo útil e melhorar a saúde ocupacional. Esses dados podem ser acedidos pelos médicos para apoiar diagnósticos e correlacionar os sintomas e problemas de saúde dos pacientes com o ambiente em que estes vivem. Como trabalho futuro, os resultados reportados nesta Tese podem ser considerados um ponto de partida para o desenvolvimento de um sistema seguro para partilha de dados com profissionais de saúde de forma a servir de suporte à decisão no diagnóstico

IoT and Sensor Networks in Industry and Society

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

The development of a real-time energy prediction framework in domestic buildings

The construction industry consumed 35% of worldwide energy, with domestic buildings accounting for 22%. Providing a healthy, positive environment in domestic buildings raised energy demand by around 80% in building operations, with thermal comfort accounting for about half of that increase. Furthermore, building energy consumption is 5 to 10 times greater than predictions given during the design phase. The discrepancy between the actual and intended design is called the performance gap. Although the term is widely used in the context of energy performance, it can also be found in indoor environmental parameters such as temperature, relative humidity, air quality, noise, and illumination. This thesis connects building performance simulation to building operational performance, focusing on real-time energy prediction for space heating in an indoor environment of domestic buildings. The work presented in this research is a technical implementation framework for examining the energy consumption of indoor space heating in real-time, focusing on energy-related thermal comfort conditions at the zone level. Unlike building performance simulation tools, The developed framework can be used beyond the design phase to encompass operations and assist in diagnosing and detecting building underperformance or performance discrepancy over time. Focusing on zone level can offer a greater understanding of the thermal state and energy usage of specific individual spaces, which can also assist in identifying performance disparity. Buildings with good indoor environmental quality are objectively assessed using simulation tools. However, the indoor environmental quality, especially thermal comfort, is experienced subjectively, making the building energy and thermal performance evaluation task challenging. The developed framework extends the use of the energy model to the operational stage by predicting thermal and energy performance based on indoor and outdoor environmental parameters. Moreover, using a parametric energy simulation and machine learning approach connected to an IoT sensor system enable users to identify thermal comfort conditions in the indoor environment and the amount of energy consumed for space heating. Finally, the research identified several lessons that can potentially inform and improve the existing domestic buildings, especially winter space heating. Following the framework, an innovative device was developed and validated using an experimental approach that focuses on real-time energy prediction of space heating. In this process, the experimental case studies' thermal comfort conditions and energy consumption were monitored and analysed to identify thermal-energy performance-related issues, also used for validating the proposed real-time energy prediction module

Urban Informatics

This open access book is the first to systematically introduce the principles of urban informatics and its application to every aspect of the city that involves its functioning, control, management, and future planning. It introduces new models and tools being developed to understand and implement these technologies that enable cities to function more efficiently – to become ‘smart’ and ‘sustainable’. The smart city has quickly emerged as computers have become ever smaller to the point where they can be embedded into the very fabric of the city, as well as being central to new ways in which the population can communicate and act. When cities are wired in this way, they have the potential to become sentient and responsive, generating massive streams of ‘big’ data in real time as well as providing immense opportunities for extracting new forms of urban data through crowdsourcing. This book offers a comprehensive review of the methods that form the core of urban informatics from various kinds of urban remote sensing to new approaches to machine learning and statistical modelling. It provides a detailed technical introduction to the wide array of tools information scientists need to develop the key urban analytics that are fundamental to learning about the smart city, and it outlines ways in which these tools can be used to inform design and policy so that cities can become more efficient with a greater concern for environment and equity

Urban Informatics

This open access book is the first to systematically introduce the principles of urban informatics and its application to every aspect of the city that involves its functioning, control, management, and future planning. It introduces new models and tools being developed to understand and implement these technologies that enable cities to function more efficiently – to become ‘smart’ and ‘sustainable’. The smart city has quickly emerged as computers have become ever smaller to the point where they can be embedded into the very fabric of the city, as well as being central to new ways in which the population can communicate and act. When cities are wired in this way, they have the potential to become sentient and responsive, generating massive streams of ‘big’ data in real time as well as providing immense opportunities for extracting new forms of urban data through crowdsourcing. This book offers a comprehensive review of the methods that form the core of urban informatics from various kinds of urban remote sensing to new approaches to machine learning and statistical modelling. It provides a detailed technical introduction to the wide array of tools information scientists need to develop the key urban analytics that are fundamental to learning about the smart city, and it outlines ways in which these tools can be used to inform design and policy so that cities can become more efficient with a greater concern for environment and equity

Urban Informatics

This open access book is the first to systematically introduce the principles of urban informatics and its application to every aspect of the city that involves its functioning, control, management, and future planning. It introduces new models and tools being developed to understand and implement these technologies that enable cities to function more efficiently – to become ‘smart’ and ‘sustainable’. The smart city has quickly emerged as computers have become ever smaller to the point where they can be embedded into the very fabric of the city, as well as being central to new ways in which the population can communicate and act. When cities are wired in this way, they have the potential to become sentient and responsive, generating massive streams of ‘big’ data in real time as well as providing immense opportunities for extracting new forms of urban data through crowdsourcing. This book offers a comprehensive review of the methods that form the core of urban informatics from various kinds of urban remote sensing to new approaches to machine learning and statistical modelling. It provides a detailed technical introduction to the wide array of tools information scientists need to develop the key urban analytics that are fundamental to learning about the smart city, and it outlines ways in which these tools can be used to inform design and policy so that cities can become more efficient with a greater concern for environment and equity

Proceedings of the 7th International Conference EEDAL 2013 Energy Efficiency in Domestic Appliances and Lighting

This book contains the papers presented at the seventh international conference on Energy Efficiency in Domestic Appliances and Lighting. EEDAL'2013 was organised in Coimbra, Portugal in September 2013. This major international conference, which was previously been staged in Florence 1997, Naples 2000, Turin 2003, London 2006, B2e0r0l9in, Copenhagen 2011 has been very successful in attracting an international community of stakeholders dealing with residential appliances, equipment, metering liagnhdti ng (including manufacturers, retailers, consumers, governments, international organisations aangde ncies, academia and experts) to discuss the progress achieved in technologies, behavioural aspects and poliacineds , the strategies that need to be implemented to further progress this important work. Potential readers who may benefit from this book include researchers, engineers, policymakers, and all those who can influence the design, selection, application, and operation of electrical appliances and lighting.JRC.F.7-Renewables and Energy Efficienc