We focus on large-scale and dense deeply embedded systemswhere, due to the large amount of information generatedby all nodes, even simple aggregate computations suchas the minimum value (MIN) of the sensor readings becomenotoriously expensive to obtain. Recent research has exploiteda dominance-based medium access control (MAC)protocol, the CAN bus, for computing aggregated quantitiesin wired systems. For example, MIN can be computedefficiently and an interpolation function which approximatessensor data in an area can be obtained efficiently aswell. Dominance-based MAC protocols have recently beenproposed for wireless channels and these protocols can beexpected to be used for achieving highly scalable aggregatecomputations in wireless systems. But no experimentaldemonstration of that is currently available in the researchliterature.In this paper, we demonstrate that highly scalable aggregatecomputations in wireless networks are possible. We doso by (i) building a new wireless hardware platform with appropriatecharacteristics for making dominance-based MACprotocols efficient, (ii) implementing dominance-based MACprotocols on this platform, (iii) implementing distributed algorithmsfor aggregate computations (MIN,MAX, Interpolation)using the new implementation of the dominance-basedMAC protocol and (iv) performing experiments to prove thatsuch highly scalable aggregate computations in wireless networksare possible. ! AbstractWe focus on large-scale and dense deeply embedded systems where, due to the large amount of information generated by all nodes, even simple aggregate computations such as the minimum value (MIN) of the sensor readings become notoriously expensive to obtain. Recent research has exploited a dominance-based medium access control (MAC) protocol, the CAN bus, for computing aggregated quantities in wired systems. For example, MIN can be computed efficiently and an interpolation function which approximates sensor data in an area can be obtained efficiently as well. Dominance-based MAC protocols have recently been proposed for wireless channels and these protocols can be expected to be used for achieving highly scalable aggregate computations in wireless systems. But no experimental demonstration is currently available in the research literature.In this paper, we demonstrate that highly scalable aggregate computations in wireless networks are possible. We do so by (i) building a new wireless hardware platform with appropriate characteristics for making dominance-based MAC protocols efficient, (ii) implementing dominance-based MAC protocols on this platform, (iii) implementing distributed algorithms for aggregate computations (MIN, MAX, Interpolation) using the new implementation of the dominance-based MAC protocol and (iv) performing experiments to prove that such highly scalable aggregate computations in wireless networks are possible.

The demand for objectivity in clinical diagnosis has been one of the greatest challenges in Biomedical Engineering. The study, development and implementation of solutions that may serve as ground truth in physical activity recognition and in medical diagnosis of chronic motor diseases is ever more imperative. This paper describes a human activity recognition framework based on feature extraction and feature selection techniques where a set of time, statistical and frequency domain features taken from 3-dimensional accelerometer sensors are extracted. In this paper, unsupervised learning is applied to the feature representation of accelerometer data to discover the activities performed by different subjects. A feature selection framework is developed in order to improve the clustering accuracy and reduce computational costs. The features which best distinguish a particular set of activities are selected from a 180 th-dimensional feature vector through machine learning algorithms. The implemented framework achieved very encouraging results in human activity recognition: an average person-dependent Adjusted Rand Index (ARI) of 99.29% ± 0.5% and a person-independent ARI of 88.57% ± 4.0% were reached.

Abstract-Wireless sensor networks (WSNs) have attracted growing interest in the last decade as an infrastructure to support a diversity of ubiquitous computing and cyber-physical systems. However, most research work has focused on protocols or on specific applications. As a result, there remains a clear lack of effective, feasible and usable system architectures that address both functional and non-functional requirements in an integrated fashion. In this paper, we outline the EMMON system architecture for large-scale, dense, real-time embedded monitoring. EMMON provides a hierarchical communication architecture together with integrated middleware and command and control software. It has been designed to use standard commercially-available technologies, while maintaining as much flexibility as possible to meet specific applications requirements. The EMMON architecture has been validated through extensive simulation and experimental evaluation, including a 300+ node test-bed, which is, to the best of our knowledge, the largest singlesite WSN test-bed in Europe to date.

Structural Health Monitoring represents the present and future of the civil engineering since, until few years ago, structural diagnosis works had been performed with few resources regarding to experimental techniques. Precisely in the field of monitoring sensors, the progress of new technologies based on wireless communications and Micro-Electro-MechanicalSystems (MEMS) are of high interest for replacing the handle difficult wired sensors. However, three major limitations of the commercial off-the-shelf technology on WSN (combination of MEMS and wireless technology) for performing dynamic monitoring were identified by means of: (1) not enough sensitivity of the accelerometers; (2) low resolution of the ADC embedded; and (3) lack of synchronization algorithms implemented. This paper presents a new prototype system conceived for performing dynamic monitoring civil engineering structures. This system was jointly conceived by a team of civil, electrical and communication engineers and is a combination of the last technology on high resolution MEMS accelerometers and the state of the art of communication technologies. Despite the fact that the prototype system needs more improvements; the results of several rounds of validation experiences confirm the feasibility for its consideration as an alternative to the conventional wired based sensors.

perpétuo e sem limites geográficos, de arquivar e publicar esta dissertação através de exemplares impressos reproduzidos em papel ou de forma digital, ou por qualquer outro meio conhecido ou que venha a ser inventado, e de a divulgar através de repositórios científicos e de admitir a sua cópia e distribuição com objectivos educacionais ou de investigação, não comerciais, desde que seja dado crédito ao autor e editor. Gamboa, for the opportunity he gave me. I am very grateful for his encouragement, guidance and for the opportunity of working with his research team. I am also very thankful to my co-adviser, Professor Ricardo Matias, for all the knowledge and enthusiasm which allowed me to achieve my goals.The opportunity to participate in this project helped me growing and it is rewarding to know that I could contribute for the research in such an important area. AbstractBenefits of long-term monitoring have drawn considerable attention in healthcare.Since the acquired data provides an important source of information to clinicians and researchers, the choice for long-term monitoring studies has become frequent.However, long-term monitoring can result in massive datasets, which makes the analysis of the acquired biosignals a challenge. In this case, visualization, which is a key point in signal analysis, presents several limitations and the annotations handling in which some machine learning algorithms depend on, turn out to be a complex task.In order to overcome these problems a novel web-based application for biosignals visualization and annotation in a fast and user friendly way was developed. This was possible through the study and implementation of a visualization model. The main process of this model, the visualization process, comprised the constitution of the domain problem, the abstraction design, the development of a multilevel visualization and the study and choice of the visualization techniques that better communicate the information carried by the data. In a second process, the visual encoding variables were the study target. Finally, the improved interaction exploration techniques were implemented where the annotation handling stands out.Three case studies are presented and discussed and a usability study supports the reliability of the implemented work. ResumoOs benefícios da monitorização de longa duração têm recebido uma atenção considerável na área da saúde. Uma vez que os dados recolhidos constituem uma importante fonte de informação para médicos e investigadores, a escolha deste tipo de estudos tem--se tornado frequente.No entanto, este tipo de monitorização pode resultar em conjuntos de dados de grandes dimensões o que torna num desafio a análise dos biosinais adquiridos. Neste caso, a visualização que é um ponto-chave na análise de sinais, apresenta muitas limitações e a manipulação de anotações da qual dependem alguns algoritmos de machine learning, torna-se uma tarefa complexa.Por forma a superar estes problemas uma inovadora aplicação baseada nas tecnologias Web para a visualização e an...

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