Exploratory Research on Developing Hadoop-based Data Analytics Tools

As we crossed past the millennium mark, slowly but steadily we were flooded by (digital) data from virtually everywhere. The challenges of big data demand new methods, techniques, and paradigms for processing data in a fast and scalable fashion. Most enterprises today need to employ data analysis technologies to remain competitive and profitable. Alas, the adoption of big data analysis technologies in Indonesia is still in its infancy, even in the academic sector. To encourage more adoptions of big data technologies, this study explored the development of Hadoop-based data analytics tools. Two case studies were used in the exploration. One is to showcase the performance comparison between Hadoop and DBMS, whereas the other is between Hadoop and a statistical analysis tool. Results clearly demonstrate that Hadoop is superior in processing a large data size. We also derive some recommendations to tune Hadoop optimally

Internet of Things for Mental Health: Open Issues in Data Acquisition, Self-Organization, Service Level Agreement, and Identity Management

The increase of mental illness cases around the world can be described as an urgent and serious global health threat. Around 500 million people suffer from mental disorders, among which depression, schizophrenia, and dementia are the most prevalent. Revolutionary technological paradigms such as the Internet of Things (IoT) provide us with new capabilities to detect, assess, and care for patients early. This paper comprehensively survey works done at the intersection between IoT and mental health disorders. We evaluate multiple computational platforms, methods and devices, as well as study results and potential open issues for the effective use of IoT systems in mental health. We particularly elaborate on relevant open challenges in the use of existing IoT solutions for mental health care, which can be relevant given the potential impairments in some mental health patients such as data acquisition issues, lack of self-organization of devices and service level agreement, and security, privacy and consent issues, among others. We aim at opening the conversation for future research in this rather emerging area by outlining possible new paths based on the results and conclusions of this work.Consejo Nacional de Ciencia y Tecnologia (CONACyT)Sonora Institute of Technology (ITSON) via the PROFAPI program PROFAPI_2020_0055Spanish Ministry of Science, Innovation and Universities (MICINN) project "Advanced Computing Architectures and Machine Learning-Based Solutions for Complex Problems in Bioinformatics, Biotechnology and Biomedicine" RTI2018-101674-B-I0

Performance of management solutions and cooperation approaches for vehicular delay-tolerant networks

A wide range of daily-life applications supported by vehicular networks attracted the interest, not only from the research community, but also from governments and the automotive industry. For example, they can be used to enable services that assist drivers on the roads (e.g., road safety, traffic monitoring), to spread commercial and entertainment contents (e.g., publicity), or to enable communications on remote or rural regions where it is not possible to have a common network infrastructure. Nonetheless, the unique properties of vehicular networks raise several challenges that greatly impact the deployment of these networks. Most of the challenges faced by vehicular networks arise from the highly dynamic network topology, which leads to short and sporadic contact opportunities, disruption, variable node density, and intermittent connectivity. This situation makes data dissemination an interesting research topic within the vehicular networking area, which is addressed by this study. The work described along this thesis is motivated by the need to propose new solutions to deal with data dissemination problems in vehicular networking focusing on vehicular delay-tolerant networks (VDTNs). To guarantee the success of data dissemination in vehicular networks scenarios it is important to ensure that network nodes cooperate with each other. However, it is not possible to ensure a fully cooperative scenario. This situation makes vehicular networks suitable to the presence of selfish and misbehavior nodes, which may result in a significant decrease of the overall network performance. Thus, cooperative nodes may suffer from the overwhelming load of services from other nodes, which comprises their performance. Trying to solve some of these problems, this thesis presents several proposals and studies on the impact of cooperation, monitoring, and management strategies on the network performance of the VDTN architecture. The main goal of these proposals is to enhance the network performance. In particular, cooperation and management approaches are exploited to improve and optimize the use of network resources. It is demonstrated the performance gains attainable in a VDTN through both types of approaches, not only in terms of bundle delivery probability, but also in terms of wasted resources. The results and achievements observed on this research work are intended to contribute to the advance of the state-of-the-art on methods and strategies for overcome the challenges that arise from the unique characteristics and conceptual design of vehicular networks.O vasto número de aplicações e cenários suportados pelas redes veiculares faz com que estas atraiam o interesse não só da comunidade científica, mas também dos governos e da indústria automóvel. A título de exemplo, estas podem ser usadas para a implementação de serviços e aplicações que podem ajudar os condutores dos veículos a tomar decisões nas estradas, para a disseminação de conteúdos publicitários, ou ainda, para permitir que existam comunicações em zonas rurais ou remotas onde não é possível ter uma infraestrutura de rede convencional. Contudo, as propriedades únicas das redes veiculares fazem com que seja necessário ultrapassar um conjunto de desafios que têm grande impacto na sua aplicabilidade. A maioria dos desafios que as redes veiculares enfrentam advêm da grande mobilidade dos veículos e da topologia de rede que está em constante mutação. Esta situação faz com que este tipo de rede seja suscetível de disrupção, que as oportunidades de contacto sejam escassas e de curta duração, e que a ligação seja intermitente. Fruto destas adversidades, a disseminação dos dados torna-se um tópico de investigação bastante promissor na área das redes veiculares e por esta mesma razão é abordada neste trabalho de investigação. O trabalho descrito nesta tese é motivado pela necessidade de propor novas abordagens para lidar com os problemas inerentes à disseminação dos dados em ambientes veiculares. Para garantir o sucesso da disseminação dos dados em ambientes veiculares é importante que este tipo de redes garanta a cooperação entre os nós da rede. Contudo, neste tipo de ambientes não é possível garantir um cenário totalmente cooperativo. Este cenário faz com que as redes veiculares sejam suscetíveis à presença de nós não cooperativos que comprometem seriamente o desempenho global da rede. Por outro lado, os nós cooperativos podem ver o seu desempenho comprometido por causa da sobrecarga de serviços que poderão suportar. Para tentar resolver alguns destes problemas, esta tese apresenta várias propostas e estudos sobre o impacto de estratégias de cooperação, monitorização e gestão de rede no desempenho das redes veiculares com ligações intermitentes (Vehicular Delay-Tolerant Networks - VDTNs). O objetivo das propostas apresentadas nesta tese é melhorar o desempenho global da rede. Em particular, as estratégias de cooperação e gestão de rede são exploradas para melhorar e optimizar o uso dos recursos da rede. Ficou demonstrado que o uso deste tipo de estratégias e metodologias contribui para um aumento significativo do desempenho da rede, não só em termos de agregados de pacotes (“bundles”) entregues, mas também na diminuição do volume de recursos desperdiçados. Os resultados observados neste trabalho procuram contribuir para o avanço do estado da arte em métodos e estratégias que visam ultrapassar alguns dos desafios que advêm das propriedades e desenho conceptual das redes veiculares

An Investigation of Proposed Techniques for Quantifying Confidence in Assurance Arguments

The use of safety cases in certification raises the question of assurance argument sufficiency and the issue of confidence (or uncertainty) in the argument's claims. Some researchers propose to model confidence quantitatively and to calculate confidence in argument conclusions. We know of little evidence to suggest that any proposed technique would deliver trustworthy results when implemented by system safety practitioners. Proponents do not usually assess the efficacy of their techniques through controlled experiment or historical study. Instead, they present an illustrative example where the calculation delivers a plausible result. In this paper, we review current proposals, claims made about them, and evidence advanced in favor of them. We then show that proposed techniques can deliver implausible results in some cases. We conclude that quantitative confidence techniques require further validation before they should be recommended as part of the basis for deciding whether an assurance argument justifies fielding a critical system

Multi-sensor data fusion in mobile devices for the identification of Activities of Daily Living

Following the recent advances in technology and the growing use of mobile devices such as smartphones, several solutions may be developed to improve the quality of life of users in the context of Ambient Assisted Living (AAL). Mobile devices have different available sensors, e.g., accelerometer, gyroscope, magnetometer, microphone and Global Positioning System (GPS) receiver, which allow the acquisition of physical and physiological parameters for the recognition of different Activities of Daily Living (ADL) and the environments in which they are performed. The definition of ADL includes a well-known set of tasks, which include basic selfcare tasks, based on the types of skills that people usually learn in early childhood, including feeding, bathing, dressing, grooming, walking, running, jumping, climbing stairs, sleeping, watching TV, working, listening to music, cooking, eating and others. On the context of AAL, some individuals (henceforth called user or users) need particular assistance, either because the user has some sort of impairment, or because the user is old, or simply because users need/want to monitor their lifestyle. The research and development of systems that provide a particular assistance to people is increasing in many areas of application. In particular, in the future, the recognition of ADL will be an important element for the development of a personal digital life coach, providing assistance to different types of users. To support the recognition of ADL, the surrounding environments should be also recognized to increase the reliability of these systems. The main focus of this Thesis is the research on methods for the fusion and classification of the data acquired by the sensors available in off-the-shelf mobile devices in order to recognize ADL in almost real-time, taking into account the large diversity of the capabilities and characteristics of the mobile devices available in the market. In order to achieve this objective, this Thesis started with the review of the existing methods and technologies to define the architecture and modules of the method for the identification of ADL. With this review and based on the knowledge acquired about the sensors available in off-the-shelf mobile devices, a set of tasks that may be reliably identified was defined as a basis for the remaining research and development to be carried out in this Thesis. This review also identified the main stages for the development of a new method for the identification of the ADL using the sensors available in off-the-shelf mobile devices; these stages are data acquisition, data processing, data cleaning, data imputation, feature extraction, data fusion and artificial intelligence. One of the challenges is related to the different types of data acquired from the different sensors, but other challenges were found, including the presence of environmental noise, the positioning of the mobile device during the daily activities, the limited capabilities of the mobile devices and others. Based on the acquired data, the processing was performed, implementing data cleaning and feature extraction methods, in order to define a new framework for the recognition of ADL. The data imputation methods were not applied, because at this stage of the research their implementation does not have influence in the results of the identification of the ADL and environments, as the features are extracted from a set of data acquired during a defined time interval and there are no missing values during this stage. The joint selection of the set of usable sensors and the identifiable set of tasks will then allow the development of a framework that, considering multi-sensor data fusion technologies and context awareness, in coordination with other information available from the user context, such as his/her agenda and the time of the day, will allow to establish a profile of the tasks that the user performs in a regular activity day. The classification method and the algorithm for the fusion of the features for the recognition of ADL and its environments needs to be deployed in a machine with some computational power, while the mobile device that will use the created framework, can perform the identification of the ADL using a much less computational power. Based on the results reported in the literature, the method chosen for the recognition of the ADL is composed by three variants of Artificial Neural Networks (ANN), including simple Multilayer Perceptron (MLP) networks, Feedforward Neural Networks (FNN) with Backpropagation, and Deep Neural Networks (DNN). Data acquisition can be performed with standard methods. After the acquisition, the data must be processed at the data processing stage, which includes data cleaning and feature extraction methods. The data cleaning method used for motion and magnetic sensors is the low pass filter, in order to reduce the noise acquired; but for the acoustic data, the Fast Fourier Transform (FFT) was applied to extract the different frequencies. When the data is clean, several features are then extracted based on the types of sensors used, including the mean, standard deviation, variance, maximum value, minimum value and median of raw data acquired from the motion and magnetic sensors; the mean, standard deviation, variance and median of the maximum peaks calculated with the raw data acquired from the motion and magnetic sensors; the five greatest distances between the maximum peaks calculated with the raw data acquired from the motion and magnetic sensors; the mean, standard deviation, variance, median and 26 Mel- Frequency Cepstral Coefficients (MFCC) of the frequencies obtained with FFT based on the raw data acquired from the microphone data; and the distance travelled calculated with the data acquired from the GPS receiver. After the extraction of the features, these will be grouped in different datasets for the application of the ANN methods and to discover the method and dataset that reports better results. The classification stage was incrementally developed, starting with the identification of the most common ADL (i.e., walking, running, going upstairs, going downstairs and standing activities) with motion and magnetic sensors. Next, the environments were identified with acoustic data, i.e., bedroom, bar, classroom, gym, kitchen, living room, hall, street and library. After the environments are recognized, and based on the different sets of sensors commonly available in the mobile devices, the data acquired from the motion and magnetic sensors were combined with the recognized environment in order to differentiate some activities without motion, i.e., sleeping and watching TV. The number of recognized activities in this stage was increased with the use of the distance travelled, extracted from the GPS receiver data, allowing also to recognize the driving activity. After the implementation of the three classification methods with different numbers of iterations, datasets and remaining configurations in a machine with high processing capabilities, the reported results proved that the best method for the recognition of the most common ADL and activities without motion is the DNN method, but the best method for the recognition of environments is the FNN method with Backpropagation. Depending on the number of sensors used, this implementation reports a mean accuracy between 85.89% and 89.51% for the recognition of the most common ADL, equals to 86.50% for the recognition of environments, and equals to 100% for the recognition of activities without motion, reporting an overall accuracy between 85.89% and 92.00%. The last stage of this research work was the implementation of the structured framework for the mobile devices, verifying that the FNN method requires a high processing power for the recognition of environments and the results reported with the mobile application are lower than the results reported with the machine with high processing capabilities used. Thus, the DNN method was also implemented for the recognition of the environments with the mobile devices. Finally, the results reported with the mobile devices show an accuracy between 86.39% and 89.15% for the recognition of the most common ADL, equal to 45.68% for the recognition of environments, and equal to 100% for the recognition of activities without motion, reporting an overall accuracy between 58.02% and 89.15%. Compared with the literature, the results returned by the implemented framework show only a residual improvement. However, the results reported in this research work comprehend the identification of more ADL than the ones described in other studies. The improvement in the recognition of ADL based on the mean of the accuracies is equal to 2.93%, but the maximum number of ADL and environments previously recognized was 13, while the number of ADL and environments recognized with the framework resulting from this research is 16. In conclusion, the framework developed has a mean improvement of 2.93% in the accuracy of the recognition for a larger number of ADL and environments than previously reported. In the future, the achievements reported by this PhD research may be considered as a start point of the development of a personal digital life coach, but the number of ADL and environments recognized by the framework should be increased and the experiments should be performed with different types of devices (i.e., smartphones and smartwatches), and the data imputation and other machine learning methods should be explored in order to attempt to increase the reliability of the framework for the recognition of ADL and its environments.Após os recentes avanços tecnológicos e o crescente uso dos dispositivos móveis, como por exemplo os smartphones, várias soluções podem ser desenvolvidas para melhorar a qualidade de vida dos utilizadores no contexto de Ambientes de Vida Assistida (AVA) ou Ambient Assisted Living (AAL). Os dispositivos móveis integram vários sensores, tais como acelerómetro, giroscópio, magnetómetro, microfone e recetor de Sistema de Posicionamento Global (GPS), que permitem a aquisição de vários parâmetros físicos e fisiológicos para o reconhecimento de diferentes Atividades da Vida Diária (AVD) e os seus ambientes. A definição de AVD inclui um conjunto bem conhecido de tarefas que são tarefas básicas de autocuidado, baseadas nos tipos de habilidades que as pessoas geralmente aprendem na infância. Essas tarefas incluem alimentar-se, tomar banho, vestir-se, fazer os cuidados pessoais, caminhar, correr, pular, subir escadas, dormir, ver televisão, trabalhar, ouvir música, cozinhar, comer, entre outras. No contexto de AVA, alguns indivíduos (comumente chamados de utilizadores) precisam de assistência particular, seja porque o utilizador tem algum tipo de deficiência, seja porque é idoso, ou simplesmente porque o utilizador precisa/quer monitorizar e treinar o seu estilo de vida. A investigação e desenvolvimento de sistemas que fornecem algum tipo de assistência particular está em crescente em muitas áreas de aplicação. Em particular, no futuro, o reconhecimento das AVD é uma parte importante para o desenvolvimento de um assistente pessoal digital, fornecendo uma assistência pessoal de baixo custo aos diferentes tipos de pessoas. pessoas. Para ajudar no reconhecimento das AVD, os ambientes em que estas se desenrolam devem ser reconhecidos para aumentar a fiabilidade destes sistemas. O foco principal desta Tese é o desenvolvimento de métodos para a fusão e classificação dos dados adquiridos a partir dos sensores disponíveis nos dispositivos móveis, para o reconhecimento quase em tempo real das AVD, tendo em consideração a grande diversidade das características dos dispositivos móveis disponíveis no mercado. Para atingir este objetivo, esta Tese iniciou-se com a revisão dos métodos e tecnologias existentes para definir a arquitetura e os módulos do novo método de identificação das AVD. Com esta revisão da literatura e com base no conhecimento adquirido sobre os sensores disponíveis nos dispositivos móveis disponíveis no mercado, um conjunto de tarefas que podem ser identificadas foi definido para as pesquisas e desenvolvimentos desta Tese. Esta revisão também identifica os principais conceitos para o desenvolvimento do novo método de identificação das AVD, utilizando os sensores, são eles: aquisição de dados, processamento de dados, correção de dados, imputação de dados, extração de características, fusão de dados e extração de resultados recorrendo a métodos de inteligência artificial. Um dos desafios está relacionado aos diferentes tipos de dados adquiridos pelos diferentes sensores, mas outros desafios foram encontrados, sendo os mais relevantes o ruído ambiental, o posicionamento do dispositivo durante a realização das atividades diárias, as capacidades limitadas dos dispositivos móveis. As diferentes características das pessoas podem igualmente influenciar a criação dos métodos, escolhendo pessoas com diferentes estilos de vida e características físicas para a aquisição e identificação dos dados adquiridos a partir de sensores. Com base nos dados adquiridos, realizou-se o processamento dos dados, implementando-se métodos de correção dos dados e a extração de características, para iniciar a criação do novo método para o reconhecimento das AVD. Os métodos de imputação de dados foram excluídos da implementação, pois não iriam influenciar os resultados da identificação das AVD e dos ambientes, na medida em que são utilizadas as características extraídas de um conjunto de dados adquiridos durante um intervalo de tempo definido. A seleção dos sensores utilizáveis, bem como das AVD identificáveis, permitirá o desenvolvimento de um método que, considerando o uso de tecnologias para a fusão de dados adquiridos com múltiplos sensores em coordenação com outras informações relativas ao contexto do utilizador, tais como a agenda do utilizador, permitindo estabelecer um perfil de tarefas que o utilizador realiza diariamente. Com base nos resultados obtidos na literatura, o método escolhido para o reconhecimento das AVD são as diferentes variantes das Redes Neuronais Artificiais (RNA), incluindo Multilayer Perceptron (MLP), Feedforward Neural Networks (FNN) with Backpropagation and Deep Neural Networks (DNN). No final, após a criação dos métodos para cada fase do método para o reconhecimento das AVD e ambientes, a implementação sequencial dos diferentes métodos foi realizada num dispositivo móvel para testes adicionais. Após a definição da estrutura do método para o reconhecimento de AVD e ambientes usando dispositivos móveis, verificou-se que a aquisição de dados pode ser realizada com os métodos comuns. Após a aquisição de dados, os mesmos devem ser processados no módulo de processamento de dados, que inclui os métodos de correção de dados e de extração de características. O método de correção de dados utilizado para sensores de movimento e magnéticos é o filtro passa-baixo de modo a reduzir o ruído, mas para os dados acústicos, a Transformada Rápida de Fourier (FFT) foi aplicada para extrair as diferentes frequências. Após a correção dos dados, as diferentes características foram extraídas com base nos tipos de sensores usados, sendo a média, desvio padrão, variância, valor máximo, valor mínimo e mediana de dados adquiridos pelos sensores magnéticos e de movimento, a média, desvio padrão, variância e mediana dos picos máximos calculados com base nos dados adquiridos pelos sensores magnéticos e de movimento, as cinco maiores distâncias entre os picos máximos calculados com os dados adquiridos dos sensores de movimento e magnéticos, a média, desvio padrão, variância e 26 Mel-Frequency Cepstral Coefficients (MFCC) das frequências obtidas com FFT com base nos dados obtidos a partir do microfone, e a distância calculada com os dados adquiridos pelo recetor de GPS. Após a extração das características, as mesmas são agrupadas em diferentes conjuntos de dados para a aplicação dos métodos de RNA de modo a descobrir o método e o conjunto de características que reporta melhores resultados. O módulo de classificação de dados foi incrementalmente desenvolvido, começando com a identificação das AVD comuns com sensores magnéticos e de movimento, i.e., andar, correr, subir escadas, descer escadas e parado. Em seguida, os ambientes são identificados com dados de sensores acústicos, i.e., quarto, bar, sala de aula, ginásio, cozinha, sala de estar, hall, rua e biblioteca. Com base nos ambientes reconhecidos e os restantes sensores disponíveis nos dispositivos móveis, os dados adquiridos dos sensores magnéticos e de movimento foram combinados com o ambiente reconhecido para diferenciar algumas atividades sem movimento (i.e., dormir e ver televisão), onde o número de atividades reconhecidas nesta fase aumenta com a fusão da distância percorrida, extraída a partir dos dados do recetor GPS, permitindo também reconhecer a atividade de conduzir. Após a implementação dos três métodos de classificação com diferentes números de iterações, conjuntos de dados e configurações numa máquina com alta capacidade de processamento, os resultados relatados provaram que o melhor método para o reconhecimento das atividades comuns de AVD e atividades sem movimento é o método DNN, mas o melhor método para o reconhecimento de ambientes é o método FNN with Backpropagation. Dependendo do número de sensores utilizados, esta implementação reporta uma exatidão média entre 85,89% e 89,51% para o reconhecimento das AVD comuns, igual a 86,50% para o reconhecimento de ambientes, e igual a 100% para o reconhecimento de atividades sem movimento, reportando uma exatidão global entre 85,89% e 92,00%. A última etapa desta Tese foi a implementação do método nos dispositivos móveis, verificando que o método FNN requer um alto poder de processamento para o reconhecimento de ambientes e os resultados reportados com estes dispositivos são inferiores aos resultados reportados com a máquina com alta capacidade de processamento utilizada no desenvolvimento do método. Assim, o método DNN foi igualmente implementado para o reconhecimento dos ambientes com os dispositivos móveis. Finalmente, os resultados relatados com os dispositivos móveis reportam uma exatidão entre 86,39% e 89,15% para o reconhecimento das AVD comuns, igual a 45,68% para o reconhecimento de ambientes, e igual a 100% para o reconhecimento de atividades sem movimento, reportando uma exatidão geral entre 58,02% e 89,15%. Com base nos resultados relatados na literatura, os resultados do método desenvolvido mostram uma melhoria residual, mas os resultados desta Tese identificam mais AVD que os demais estudos disponíveis na literatura. A melhoria no reconhecimento das AVD com base na média das exatidões é igual a 2,93%, mas o número máximo de AVD e ambientes reconhecidos pelos estudos disponíveis na literatura é 13, enquanto o número de AVD e ambientes reconhecidos com o método implementado é 16. Assim, o método desenvolvido tem uma melhoria de 2,93% na exatidão do reconhecimento num maior número de AVD e ambientes. Como trabalho futuro, os resultados reportados nesta Tese podem ser considerados um ponto de partida para o desenvolvimento de um assistente digital pessoal, mas o número de ADL e ambientes reconhecidos pelo método deve ser aumentado e as experiências devem ser repetidas com diferentes tipos de dispositivos móveis (i.e., smartphones e smartwatches), e os métodos de imputação e outros métodos de classificação de dados devem ser explorados de modo a tentar aumentar a confiabilidade do método para o reconhecimento das AVD e ambientes

Seamless Multimodal Biometrics for Continuous Personalised Wellbeing Monitoring

Artificially intelligent perception is increasingly present in the lives of every one of us. Vehicles are no exception, (...) In the near future, pattern recognition will have an even stronger role in vehicles, as self-driving cars will require automated ways to understand what is happening around (and within) them and act accordingly. (...) This doctoral work focused on advancing in-vehicle sensing through the research of novel computer vision and pattern recognition methodologies for both biometrics and wellbeing monitoring. The main focus has been on electrocardiogram (ECG) biometrics, a trait well-known for its potential for seamless driver monitoring. Major efforts were devoted to achieving improved performance in identification and identity verification in off-the-person scenarios, well-known for increased noise and variability. Here, end-to-end deep learning ECG biometric solutions were proposed and important topics were addressed such as cross-database and long-term performance, waveform relevance through explainability, and interlead conversion. Face biometrics, a natural complement to the ECG in seamless unconstrained scenarios, was also studied in this work. The open challenges of masked face recognition and interpretability in biometrics were tackled in an effort to evolve towards algorithms that are more transparent, trustworthy, and robust to significant occlusions. Within the topic of wellbeing monitoring, improved solutions to multimodal emotion recognition in groups of people and activity/violence recognition in in-vehicle scenarios were proposed. At last, we also proposed a novel way to learn template security within end-to-end models, dismissing additional separate encryption processes, and a self-supervised learning approach tailored to sequential data, in order to ensure data security and optimal performance. (...)Comment: Doctoral thesis presented and approved on the 21st of December 2022 to the University of Port

New Secure IoT Architectures, Communication Protocols and User Interaction Technologies for Home Automation, Industrial and Smart Environments

Programa Oficial de Doutoramento en Tecnoloxías da Información e das Comunicacións en Redes Móbiles. 5029V01Tese por compendio de publicacións[Abstract] The Internet of Things (IoT) presents a communication network where heterogeneous physical devices such as vehicles, homes, urban infrastructures or industrial machinery are interconnected and share data. For these communications to be successful, it is necessary to integrate and embed electronic devices that allow for obtaining environmental information (sensors), for performing physical actuations (actuators) as well as for sending and receiving data (network interfaces). This integration of embedded systems poses several challenges. It is needed for these devices to present very low power consumption. In many cases IoT nodes are powered by batteries or constrained power supplies. Moreover, the great amount of devices needed in an IoT network makes power e ciency one of the major concerns of these deployments, due to the cost and environmental impact of the energy consumption. This need for low energy consumption is demanded by resource constrained devices, con icting with the second major concern of IoT: security and data privacy. There are critical urban and industrial systems, such as tra c management, water supply, maritime control, railway control or high risk industrial manufacturing systems such as oil re neries that will obtain great bene ts from IoT deployments, for which non-authorized access can posse severe risks for public safety. On the other hand, both these public systems and the ones deployed on private environments (homes, working places, malls) present a risk for the privacy and security of their users. These IoT deployments need advanced security mechanisms, both to prevent access to the devices and to protect the data exchanged by them. As a consequence, it is needed to improve two main aspects: energy e ciency of IoT devices and the use of lightweight security mechanisms that can be implemented by these resource constrained devices but at the same time guarantee a fair degree of security. The huge amount of data transmitted by this type of networks also presents another challenge. There are big data systems capable of processing large amounts of data, but with IoT the granularity and dispersion of the generated information presents a new scenario very di erent from the one existing nowadays. Forecasts anticipate that there will be a growth from the 15 billion installed devices in 2015 to more than 75 billion devices in 2025. Moreover, there will be much more services exploiting the data produced by these networks, meaning the resulting tra c will be even higher. The information must not only be processed in real time, but data mining processes will have to be performed to historical data. The main goal of this Ph.D. thesis is to analyze each one of the previously described challenges and to provide solutions that allow for an adequate adoption of IoT in Industrial, domestic and, in general, any scenario that can obtain any bene t from the interconnection and exibility that IoT brings.[Resumen] La internet de las cosas (IoT o Internet of Things) representa una red de intercomunicaciones en la que participan dispositivos físicos de toda índole, como vehículos, viviendas, electrodomésticos, infraestructuras urbanas o maquinaria y dispositivos industriales. Para que esta comunicación se pueda llevar a cabo es necesario integrar elementos electr onicos que permitan obtener informaci on del entorno (sensores), realizar acciones f sicas (actuadores) y enviar y recibir la informaci on necesaria (interfaces de comunicaciones de red). La integración y uso de estos sistemas electrónicos embebidos supone varios retos. Es necesario que dichos dispositivos presenten un consumo reducido. En muchos casos deberían ser alimentados por baterías o fuentes de alimentación limitadas. Además, la gran cantidad de dispositivos que involucra la IoT hace necesario que la e ciencia energética de los mismos sea una de las principales preocupaciones, por el coste e implicaciones medioambientales que supone el consumo de electricidad de los mismos. Esta necesidad de limitar el consumo provoca que dichos dispositivos tengan unas prestaciones muy limitadas, lo que entra en conflicto con la segunda mayor preocupación de la IoT: la seguridad y privacidad de los datos. Por un lado existen sistemas críticos urbanos e industriales, como puede ser la regulación del tráfi co, el control del suministro de agua, el control marítimo, el control ferroviario o los sistemas de producción industrial de alto riesgo, como refi nerías, que son claros candidatos a benefi ciarse de la IoT, pero cuyo acceso no autorizado supone graves problemas de seguridad ciudadana. Por otro lado, tanto estos sistemas de naturaleza publica, como los que se desplieguen en entornos privados (viviendas, entornos de trabajo o centros comerciales, entre otros) suponen un riesgo para la privacidad y también para la seguridad de los usuarios. Todo esto hace que sean necesarios mecanismos de seguridad avanzados, tanto de acceso a los dispositivos como de protección de los datos que estos intercambian. En consecuencia, es necesario avanzar en dos aspectos principales: la e ciencia energética de los dispositivos y el uso de mecanismos de seguridad e ficientes, tanto computacional como energéticamente, que permitan la implantación de la IoT sin comprometer la seguridad y la privacidad de los usuarios. Por otro lado, la ingente cantidad de información que estos sistemas puede llegar a producir presenta otros dos retos que deben ser afrontados. En primer lugar, el tratamiento y análisis de datos toma una nueva dimensión. Existen sistemas de big data capaces de procesar cantidades enormes de información, pero con la internet de las cosas la granularidad y dispersión de los datos plantean un escenario muy distinto al actual. La previsión es pasar de 15.000.000.000 de dispositivos instalados en 2015 a más de 75.000.000.000 en 2025. Además existirán multitud de servicios que harán un uso intensivo de estos dispositivos y de los datos que estos intercambian, por lo que el volumen de tráfico será todavía mayor. Asimismo, la información debe ser procesada tanto en tiempo real como a posteriori sobre históricos, lo que permite obtener información estadística muy relevante en diferentes entornos. El principal objetivo de la presente tesis doctoral es analizar cada uno de estos retos (e ciencia energética, seguridad, procesamiento de datos e interacción con el usuario) y plantear soluciones que permitan una correcta adopción de la internet de las cosas en ámbitos industriales, domésticos y en general en cualquier escenario que se pueda bene ciar de la interconexión y flexibilidad de acceso que proporciona el IoT.[Resumo] O internet das cousas (IoT ou Internet of Things) representa unha rede de intercomunicaci óns na que participan dispositivos físicos moi diversos, coma vehículos, vivendas, electrodomésticos, infraestruturas urbanas ou maquinaria e dispositivos industriais. Para que estas comunicacións se poidan levar a cabo é necesario integrar elementos electrónicos que permitan obter información da contorna (sensores), realizar accións físicas (actuadores) e enviar e recibir a información necesaria (interfaces de comunicacións de rede). A integración e uso destes sistemas electrónicos integrados supón varios retos. En primeiro lugar, é necesario que estes dispositivos teñan un consumo reducido. En moitos casos deberían ser alimentados por baterías ou fontes de alimentación limitadas. Ademais, a gran cantidade de dispositivos que se empregan na IoT fai necesario que a e ciencia enerxética dos mesmos sexa unha das principais preocupacións, polo custo e implicacións medioambientais que supón o consumo de electricidade dos mesmos. Esta necesidade de limitar o consumo provoca que estes dispositivos teñan unhas prestacións moi limitadas, o que entra en con ito coa segunda maior preocupación da IoT: a seguridade e privacidade dos datos. Por un lado existen sistemas críticos urbanos e industriais, como pode ser a regulación do tráfi co, o control de augas, o control marítimo, o control ferroviario ou os sistemas de produción industrial de alto risco, como refinerías, que son claros candidatos a obter benefi cios da IoT, pero cuxo acceso non autorizado supón graves problemas de seguridade cidadá. Por outra parte tanto estes sistemas de natureza pública como os que se despreguen en contornas privadas (vivendas, contornas de traballo ou centros comerciais entre outros) supoñen un risco para a privacidade e tamén para a seguridade dos usuarios. Todo isto fai que sexan necesarios mecanismos de seguridade avanzados, tanto de acceso aos dispositivos como de protección dos datos que estes intercambian. En consecuencia, é necesario avanzar en dous aspectos principais: a e ciencia enerxética dos dispositivos e o uso de mecanismos de seguridade re cientes, tanto computacional como enerxéticamente, que permitan o despregue da IoT sen comprometer a seguridade e a privacidade dos usuarios. Por outro lado, a inxente cantidade de información que estes sistemas poden chegar a xerar presenta outros retos que deben ser tratados. O tratamento e a análise de datos toma unha nova dimensión. Existen sistemas de big data capaces de procesar cantidades enormes de información, pero coa internet das cousas a granularidade e dispersión dos datos supón un escenario moi distinto ao actual. A previsión e pasar de 15.000.000.000 de dispositivos instalados no ano 2015 a m ais de 75.000.000.000 de dispositivos no ano 2025. Ademais existirían multitude de servizos que farían un uso intensivo destes dispositivos e dos datos que intercambian, polo que o volume de tráfico sería aínda maior. Do mesmo xeito a información debe ser procesada tanto en tempo real como posteriormente sobre históricos, o que permite obter información estatística moi relevante en diferentes contornas. O principal obxectivo da presente tese doutoral é analizar cada un destes retos (e ciencia enerxética, seguridade, procesamento de datos e interacción co usuario) e propor solucións que permitan unha correcta adopción da internet das cousas en ámbitos industriais, domésticos e en xeral en todo aquel escenario que se poda bene ciar da interconexión e flexibilidade de acceso que proporciona a IoT

Citizen Science: Reducing Risk and Building Resilience to Natural Hazards

Natural hazards are becoming increasingly frequent within the context of climate change—making reducing risk and building resilience against these hazards more crucial than ever. An emerging shift has been noted from broad-scale, top-down risk and resilience assessments toward more participatory, community-based, bottom-up approaches. Arguably, non-scientist local stakeholders have always played an important role in risk knowledge management and resilience building. Rapidly developing information and communication technologies such as the Internet, smartphones, and social media have already demonstrated their sizeable potential to make knowledge creation more multidirectional, decentralized, diverse, and inclusive (Paul et al., 2018). Combined with technologies for robust and low-cost sensor networks, various citizen science approaches have emerged recently (e.g., Haklay, 2012; Paul et al., 2018) as a promising direction in the provision of extensive, real-time information for risk management (as well as improving data provision in data-scarce regions). It can serve as a means of educating and empowering communities and stakeholders that are bypassed by more traditional knowledge generation processes. This Research Topic compiles 13 contributions that interrogate the manifold ways in which citizen science has been interpreted to reduce risk against hazards that are (i) water-related (i.e., floods, hurricanes, drought, landslides); (ii) deep-earth-related (i.e., earthquakes and volcanoes); and (iii) responding to global environmental change such as sea-level rise. We have sought to analyse the particular failures and successes of natural hazards-related citizen science projects: the objective is to obtain a clearer understanding of “best practice” in a citizen science context

Usability of disaster apps : understanding the perspectives of the public as end-users : a dissertation presented in partial fulfilment of the requirements for the degree of Doctor of Philosophy in Emergency Management at Massey University, Wellington, New Zealand

Listed in 2020 Dean's List of Exceptional ThesesMultiple smartphone applications (apps) exist that can enhance the public’s resilience to disasters. Despite the capabilities of these apps, they can only be effective if users find them usable. Availability does not automatically translate to usability nor does it guarantee continued usage by the target users. A disaster app will be of little or no value if a user abandons it after the initial download. It is, therefore, essential to understand the users’ perspectives on the usability of disaster apps. In the context of disaster apps, usability entails providing the elements that effectively facilitate users in retrieving critical information, and thus enabling them to make decisions during crises. Establishing good usability for effective systems relies upon focussing on the user whereby technological solutions match the user’s needs and expectations. However, most studies on the usability of disaster context technologies have been conducted with emergency responders, and only a few have investigated the publics’ perspectives as end-users. This doctoral project, written within a ‘PhD-thesis-with-publication’ format, addresses this gap by investigating the usability of disaster apps through the perspectives of the public end-users. The investigation takes an explicitly perceived usability standpoint where the experiences of the end-users are prioritised. Data analysis involved user-centric information to understand the public’s context and the mechanisms of disaster app usability. A mixed methods approach incorporates the qualitative analysis of app store data of 1,405 user reviews from 58 existing disaster apps, the quantitative analysis of 271 survey responses from actual disaster app users, and the qualitative analysis of usability inquiries with 18 members of the public. Insights gathered from this doctoral project highlight that end-users do not anticipate using disaster apps frequently, which poses particular challenges. Furthermore, despite the anticipated low frequency of use, because of the life-safety association of disasters apps, end-users have an expectation that the apps can operate with adequate usability when needed. This doctoral project provides focussed outcomes that consider such user perspectives. First, an app store analysis investigating user reviews identified new usability concerns particular to disaster apps. It highlighted users’ opinion on phone resource usage and relevance of content, among others. More importantly, it defined a new usability factor, app dependability, relating to the life-safety context of disaster apps. App dependability is the degree to which users’ perceive that an app can operate dependably during critical scenarios. Second, the quantitative results from this research have contributed towards producing a usability-continuance model, highlighting the usability factors that affect end-users’ intention to keep or uninstall a disaster app. The key influences for users’ intention to keep disaster apps are: (1) users’ perceptions as to whether the app delivers its function (app utility), (2) whether it does so dependably (app dependability), and (3) whether it presents information that can be easily understood (user-interface output). Subsequently, too much focus on (4) user-interface graphics and (5) user-interface input can encourage users to uninstall apps. Third, the results from the qualitative analysis of the inquiry data provide a basis for developing guidelines for disaster app usability. In the expectation of low level of engagement with disaster app users, the guidelines list recommendations addressing information salience, cognitive load, and trust. This doctoral project provides several contributions to the body of knowledge for usability and disaster apps. It reiterates the importance of investigating the usability of technological products for disasters and showcases the value of user-centric data in understanding usability. It has investigated usability with particular attention to the end-users’ perspectives on the context of disaster apps and, thus, produces a theoretical usability-continuance model to advance disaster app usability research and usability guidelines to encourage responsible design in practice

An Approach to Automatically Distribute and Access Knowledge within Networked Embedded Systems in Factory Automation

This thesis presents a novel approach for automatically distribute and access knowledge within factory automation systems built by networked embedded systems. Developments on information, communication and computational technologies are making possible the distribution of tasks within different control resources, resources which are networked and working towards a common objective optimizing desired parameters. A fundamental task for introducing autonomy to these systems, is the option for represent knowledge, distributed within the automation network and to ensure its access by providing access mechanisms. This research work focuses on the processes for automatically distribute and access the knowledge.Recently, the industrial world has embraced service-oriented as architectural (SOA) patterns for relaxing the software integration costs of factory automation systems. This pattern defines a services provider offering a particular functionality, and service requesters which are entities looking for getting their needs satisfied. Currently, there are a few technologies allowing to implement a SOA solution, among those, Web Technologies are gaining special attention for their solid presence in other application fields. Providers and services using Web technologies for expressing their needs and skills are called Web Services. One of the main advantage of services is the no need for the service requester to know how the service provider is accomplishing the functionality or where the execution of the service is taking place. This benefit is recently stressed by the irruption of Cloud Computing, allowing the execution of certain process by the cloud resources.The caption of human knowledge and the representation of that knowledge in a machine interpretable manner has been an interesting research topic for the last decades. A well stablished mechanism for the representation of knowledge is the utilization of Ontologies. This mechanism allows machines to access that knowledge and use reasoning engines in order to create reasoning machines. The presence of a knowledge base allows as clearly the better identification of the web services, which is achievable by adding semantic notations to the service descriptors. The resulting services are called semantic web services.With the latest advances on computational resources, system can be built by a large number of constrained devices, yet easily connected, building a network of computational nodes, nodes that will be dedicated to execute control and communication tasks for the systems. These tasks are commanded by high level commanding systems, such as Manufacturing Execution Systems (MES) and Enterprise Resource Planning (ERP) modules. The aforementioned technologies allow a vertical approach for communicating commanding options from MES and ERP directly to the control nodes. This scenario allows to break down monolithic MES systems into small distributed functionalities, if these functionalities use Web standards for interacting and a knowledge base as main input for information, then we are arriving to the concept of Open KnowledgeDriven MES Systems (OKD-MES).The automatic distribution of the knowledge base in an OKD-MES mechanism and the accomplishment of the reasoning process in a distributed manner are the main objectives for this research. Thus, this research work describes the decentralization and management of knowledge descriptions which are currently handled by the Representation Layer (RPL) of the OKD-MES framework. This is achieved within the encapsulation of ontology modules which may be integrated by a distributed reasoning process on incoming requests. Furthermore, this dissertation presents the concept, principles and architecture for implementing Private Local Automation Clouds (PLACs), built by CPS.The thesis is an article thesis and is composed by 9 original and referred articles and supported by 7 other articles presented by the author