Feature Extraction in Densely Sensed Environments: Extensions to Multiple Broadcast Domains

The vision of the Internet of Things (IoT) includes large and dense deployment of interconnected smart sensing and monitoring devices. This vast deployment necessitates collection and processing of large volume of measurement data. However, collecting all the measured data from individual devices on such a scale may be impractical and time consuming. Moreover, processing these measurements requires complex algorithms to extract useful information. Thus, it becomes imperative to devise distributed information processing mechanisms that identify application-specific features in a timely manner and with a low overhead. In this article, we present a feature extraction mechanism for dense networks that takes advantage of dominance-based medium access control (MAC) protocols to (i) efficiently obtain global extrema of the sensed quantities, (ii) extract local extrema, and (iii) detect the boundaries of events, by using simple transforms that nodes employ on their local data. We extend our results for a large dense network with multiple broadcast domains (MBD). We discuss and compare two approaches for addressing the challenges with MBD and we show through extensive evaluations that our proposed distributed MBD approach is fast and efficient at retrieving the most valuable measurements, independent of the number sensor nodes in the network.

Configuração de sistema de monitorização

O presente trabalho enquadra-se na área das redes de computadores, fazendo referência aos protocolos e ao conjunto de equipamentos e softwares necessários para a administração, controlo e monitorização desse tipo de infra-estruturas. Para a gestão de uma rede de dados, é essencial dispor de conhecimentos e documentação de nível técnico para representar da forma mais fiel possível a configuração da rede, seguindo passo a passo a interligação entre equipamentos existentes e oferecendo assim uma visão o mais fidedigna possível das instalações. O protocolo SNMP é utilizado em larga escala sendo praticamente um standard para a administração de redes baseadas na tecnologia TCP/IP. Este protocolo define a comunicação entre um administrador e um agente, estabelecendo o formato e o significado das mensagens trocadas entre ambos. Tem a capacidade de suportar produtos de diferentes fabricantes, permitindo ao administrador manter uma base de dados com informações relevantes da monitorização de vários equipamentos, que pode ser consultada e analisada por softwares NMS concebidos especialmente para a gestão de redes de computadores. O trabalho apresentado nesta dissertação teve como objectivo utilizar uma ferramenta NMS, para fazer a monitorização e a gestão da infra-estrutura de comunicações de forma que permitisse conhecer em tempo real o estado dos elementos de rede, ajudar no diagnóstico de possíveis problemas, instalados pela Nonius nos diversos navios da frota Douro Azul. O software NMS escolhido utiliza as potencialidades do protocolo SNMP para adquirir dados de monitorização de equipamentos de rede presentes na rede, bem como monitorizar redes remotas.

Distributed processing of large remote sensing images using MapReduce - A case of Edge Detection

Dissertation submitted in partial fulfillment of the requirements for the Degree of Master of Science in Geospatial Technologies.

Intention recognition, commitment and their roles in the evolution of cooperation

Dissertação para obtenção do Grau de Doutor em Informática

Production of ellagitannins concentrate by UF-NF from tropical highland blackberries

Dissertation presented to Faculdade de Ciências e Tecnologia, Universidade Nova de Lisboa for obtaining the master degree in Membrane Engineering

Preparation and characterization of microfiltration flat polymeric membranes for biomedical applications

Dissertation presented to Faculdade de Ciências e Tecnologia, Universidade Nova de Lisboa for obtaining the master degree in Membrane Engineering

Intrusion tolerant routing with data consensus in wireless sensor networks

Dissertação para obtenção do Grau de Mestre em Engenharia Informática

Dynamic adaptation of interaction models for stateful web services

Dissertação para obtenção do Grau de Mestre em Engenharia Informática

Epidemic store for massive scale systems

The MAP-i doctoral program of the Universities of Minho, Aveiro and Porto

Human mobility patterns at the smallest scales

We present a study on human mobility at small spatial scales. Differently from large scale mobility, recently studied through dollar-bill tracking and mobile phone data sets within one big country or continent, we report Brownian features of human mobility at smaller scales. In particular, the scaling exponents found at the smallest scales is typically close to one-half, differently from the larger values for the exponent characterizing mobility at larger scales. We carefully analyze 12-month data of the Eduroam database within the Portuguese university of Minho. A full procedure is introduced with the aim of properly characterizing the human mobility within the network of access points composing the wireless system of the university. In particular, measures of flux are introduced for estimating a distance between access points. This distance is typically non-Euclidean, since the spatial constraints at such small scales distort the continuum space on which human mobility occurs. Since two different ex- ponents are found depending on the scale human motion takes place, we raise the question at which scale the transition from Brownian to non-Brownian motion takes place. In this context, we discuss how the numerical approach can be extended to larger scales, using the full Eduroam in Europe and in Asia, for uncovering the transi- tion between both dynamical regimes.

Diurnal variability of rainfall in southwest Amazonia during the LBA-TRMM field campaign of the austral summer of 1999

The TRMM-LBA field campaign was held during the austral summer of 1999 in southwestern Amazonia. Among the major objectives, was the identification and description of the diurnal variability of rainfall in the region, associated with the different rain producing weather systems that occurred during the January-February season. By using a network of 40 digital rain gauges implemented in the state of Rondônia, and together with observations and analyses of circulation and convection, it was possible to identify details of the diurnal cycle of rainfall and the associated rainfall mechanisms. Rainfall episodes were characterized by regimes of "low-level easterly" and "westerly" winds in the context of the large-scale circulation. The westerly regime is related to an enhanced South Atlantic Convergence Zone (SACZ) and an intense and/or wide Low Level Jet (LLJ) east of the Andes, which can extend eastward towards Rondônia, even though some westerly regime episodes also show a LLJ that remains close to the foothill of the Andes. The easterly regime is related to easterly propagating systems (e.g. squall-lines) with possible weakened or less frequent LLJs and a suppressed SACZ. Diurnal variability of rainfall during westerly surface wind regime shows a characteristic maximum at late afternoon followed by a relatively weaker second maximum at early evening (2100 Local Standard Time LST). The easterly regime composite shows an early morning maximum followed by an even stronger maximum in the afternoon.

Cross-scale determinants of palm species distribution

We investigate palm species distribution, richness and abundance along the Mokoti, a seasonally-dry river of southeastern Amazon and compare it to the patterns observed at a large scale, comprising the entire Brazilian territory. A total of 694 palms belonging to 10 species were sampled at the Mokoti River basin. Although the species showed diverse distribution patterns, we found that local palm abundance, richness and tree basal area were significantly higher from the hills to the bottomlands of the study region, revealing a positive association of these measures with moisture. The analyses at the larger spatial scale also showed a strong influence of vapor pressure (a measure of moisture content of the air, in turn modulated by temperature) and seasonality in temperature: the richest regions were those where temperature and humidity were simultaneously high, and which also presented a lower degree of seasonality in temperature. These results indicate that the distribution of palms seems to be strongly associated with climatic variables, supporting the idea that, by 'putting all the eggs in one basket' (a consequence of survival depending on the preservation of a single irreplaceable bud), palms have become vulnerable to extreme environmental conditions. Hence, their distribution is concentrated in those tropical and sub-tropical regions with constant conditions of (mild to high) temperature and moisture all year round.

A Grid Supercomputing Enviroment for High Demand Computational Applications

Despite the huge increase in processor and interprocessor network performace, many computational problems remain unsolved due to lack of some critical resources such as floating point sustained performance, memory bandwidth, etc... Examples of these problems are found in areas of climate research, biology, astrophysics, high energy physics (montecarlo simulations) and artificial intelligence, among others. For some of these problems, computing resources of a single supercomputing facility can be 1 or 2 orders of magnitude apart from the resources needed to solve some them. Supercomputer centers have to face an increasing demand on processing performance, with the direct consequence of an increasing number of processors and systems, resulting in a more difficult administration of HPC resources and the need for more physical space, higher electrical power consumption and improved air conditioning, among other problems. Some of the previous problems can´t be easily solved, so grid computing, intended as a technology enabling the addition and consolidation of computing power, can help in solving large scale supercomputing problems. In this document, we describe how 2 supercomputing facilities in Spain joined their resources to solve a problem of this kind. The objectives of this experience were, among others, to demonstrate that such a cooperation can enable the solution of bigger dimension problems and to measure the efficiency that could be achieved. In this document we show some preliminary results of this experience and to what extend these objectives were achieved.

Multi-scale support vector algorithms for hot spot detection and modelling

The algorithmic approach to data modelling has developed rapidly these last years, in particular methods based on data mining and machine learning have been used in a growing number of applications. These methods follow a data-driven methodology, aiming at providing the best possible generalization and predictive abilities instead of concentrating on the properties of the data model. One of the most successful groups of such methods is known as Support Vector algorithms. Following the fruitful developments in applying Support Vector algorithms to spatial data, this paper introduces a new extension of the traditional support vector regression (SVR) algorithm. This extension allows for the simultaneous modelling of environmental data at several spatial scales. The joint influence of environmental processes presenting different patterns at different scales is here learned automatically from data, providing the optimum mixture of short and large-scale models. The method is adaptive to the spatial scale of the data. With this advantage, it can provide efficient means to model local anomalies that may typically arise in situations at an early phase of an environmental emergency. However, the proposed approach still requires some prior knowledge on the possible existence of such short-scale patterns. This is a possible limitation of the method for its implementation in early warning systems. The purpose of this paper is to present the multi-scale SVR model and to illustrate its use with an application to the mapping of Cs137 activity given the measurements taken in the region of Briansk following the Chernobyl accident.

Modeling resting-state functional networks when the cortex falls asleep: local and global changes.

The transition from wakefulness to sleep represents the most conspicuous change in behavior and the level of consciousness occurring in the healthy brain. It is accompanied by similarly conspicuous changes in neural dynamics, traditionally exemplified by the change from "desynchronized" electroencephalogram activity in wake to globally synchronized slow wave activity of early sleep. However, unit and local field recordings indicate that the transition is more gradual than it might appear: On one hand, local slow waves already appear during wake; on the other hand, slow sleep waves are only rarely global. Studies with functional magnetic resonance imaging also reveal changes in resting-state functional connectivity (FC) between wake and slow wave sleep. However, it remains unclear how resting-state networks may change during this transition period. Here, we employ large-scale modeling of the human cortico-cortical anatomical connectivity to evaluate changes in resting-state FC when the model "falls asleep" due to the progressive decrease in arousal-promoting neuromodulation. When cholinergic neuromodulation is parametrically decreased, local slow waves appear, while the overall organization of resting-state networks does not change. Furthermore, we show that these local slow waves are structured macroscopically in networks that resemble the resting-state networks. In contrast, when the neuromodulator decrease further to very low levels, slow waves become global and resting-state networks merge into a single undifferentiated, broadly synchronized network.