5,420 research outputs found
An Analytics Platform for Integrating and Computing Spatio-Temporal Metrics
In large-scale context-aware applications, a central design concern is capturing, managing
and acting upon location and context data. The ability to understand the collected data and define
meaningful contextual events, based on one or more incoming (contextual) data streams, both for
a single and multiple users, is hereby critical for applications to exhibit location- and context-aware
behaviour. In this article, we describe a context-aware, data-intensive metrics platform —focusing
primarily on its geospatial support—that allows exactly this: to define and execute metrics, which
capture meaningful spatio-temporal and contextual events relevant for the application realm.
The platform (1) supports metrics definition and execution; (2) provides facilities for real-time,
in-application actions upon metrics execution results; (3) allows post-hoc analysis and visualisation
of collected data and results. It hereby offers contextual and geospatial data management and
analytics as a service, and allow context-aware application developers to focus on their core
application logic. We explain the core platform and its ecosystem of supporting applications and
tools, elaborate the most important conceptual features, and discuss implementation realised through
a distributed, micro-service based cloud architecture. Finally, we highlight possible application fields,
and present a real-world case study in the realm of psychological health
A study of existing Ontologies in the IoT-domain
Several domains have adopted the increasing use of IoT-based devices to
collect sensor data for generating abstractions and perceptions of the real
world. This sensor data is multi-modal and heterogeneous in nature. This
heterogeneity induces interoperability issues while developing cross-domain
applications, thereby restricting the possibility of reusing sensor data to
develop new applications. As a solution to this, semantic approaches have been
proposed in the literature to tackle problems related to interoperability of
sensor data. Several ontologies have been proposed to handle different aspects
of IoT-based sensor data collection, ranging from discovering the IoT sensors
for data collection to applying reasoning on the collected sensor data for
drawing inferences. In this paper, we survey these existing semantic ontologies
to provide an overview of the recent developments in this field. We highlight
the fundamental ontological concepts (e.g., sensor-capabilities and
context-awareness) required for an IoT-based application, and survey the
existing ontologies which include these concepts. Based on our study, we also
identify the shortcomings of currently available ontologies, which serves as a
stepping stone to state the need for a common unified ontology for the IoT
domain.Comment: Submitted to Elsevier JWS SI on Web semantics for the Internet/Web of
Thing
A Methodology for Trustworthy IoT in Healthcare-Related Environments
The transition to the so-called retirement years, comes with the freedom to pursue old passions
and hobbies that were not possible to do in the past busy life. Unfortunately, that freedom
does not come alone, as the previous young years are gone, and the body starts to feel the
time that passed. The necessity to adapt elder way of living, grows as they become more prone
to health problems. Often, the solution for the attention required by the elders is nursing
homes, or similar, that take away their so cherished independence.
IoT has the great potential to help elder citizens stay healthier at home, since it has the
possibility to connect and create non-intrusive systems capable of interpreting data and act
accordingly. With that capability, comes the responsibility to ensure that the collected data is
reliable and trustworthy, as human wellbeing may rely on it. Addressing this uncertainty is the
motivation for the presented work.
The proposed methodology to reduce this uncertainty and increase confidence relies on
a data fusion and a redundancy approach, using a sensor set. Since the scope of wellbeing
environment is wide, this thesis focuses its proof of concept on the detection of falls inside
home environments, through an android app using an accelerometer sensor and a micro-
phone. The experimental results demonstrates that the implemented system has more than
80% of reliable performance and can provide trustworthy results. Currently the app is being
tested also in the frame of the European Union projects Smart4Health and Smart Bear.A transição para os chamados anos de reforma, vem com a liberdade de perseguir velhas pai-
xões e passatempos que na passada vida ocupada não eram possíveis de realizar. Infelizmente,
essa liberdade não vem sozinha, uma vez que os anos jovens anteriores terminaram, e o corpo
começa a sentir o tempo que passou. A necessidade de adaptar o modo de vida dos menos
jovens, cresce à medida que estes se tornam mais propensos a problemas de saúde. Muitas
vezes, a solução para a atenção que os mais idosos necessitam são os lares de idosos, ou
similares, que lhes tiram a tão querida independência.
IoT tem o grande potencial de ajudar os cidadãos idosos a permanecerem mais saudá-
veis em casa, uma vez que tem a possibilidade de se ligar e criar sistemas não intrusivos capa-
zes de interpretar dados e agir em conformidade. Com essa capacidade, vem a responsabili-
dade de assegurar que os dados recolhidos são fiáveis e de confiança, uma vez que o bem-
estar humano possa depender dos mesmos. Abordar esta incerteza é a motivação para o tra-
balho apresentado.
A metodologia proposta para reduzir esta incerteza e aumentar a confiança no sistema
baseia-se numa fusão de dados e numa abordagem de redundância, utilizando um conjunto
de sensores. Uma vez que o assunto de bem-estar e saúde é vasto, esta tese concentra a sua
prova de conceito na deteção de quedas dentro de ambientes domésticos, através de uma
aplicação android, utilizando um sensor de acelerómetro e um microfone. Os resultados expe-
rimentais demonstram que o sistema implementado tem um desempenho superior a 80% e
pode fornecer dados fiáveis. Atualmente a aplicação está a ser testada também no âmbito dos
projetos da União Europeia Smart4Health e Smart Bear
Medical data processing and analysis for remote health and activities monitoring
Recent developments in sensor technology, wearable computing, Internet of Things (IoT), and wireless communication have given rise to research in ubiquitous healthcare and remote monitoring of human\u2019s health and activities. Health monitoring systems involve processing and analysis of data retrieved from smartphones, smart watches, smart bracelets, as well as various sensors and wearable devices. Such systems enable continuous monitoring of patients psychological and health conditions by sensing and transmitting measurements such as heart rate, electrocardiogram, body temperature, respiratory rate, chest sounds, or blood pressure. Pervasive healthcare, as a relevant application domain in this context, aims at revolutionizing the delivery of medical services through a medical assistive environment and facilitates the independent living of patients. In this chapter, we discuss (1) data collection, fusion, ownership and privacy issues; (2) models, technologies and solutions for medical data processing and analysis; (3) big medical data analytics for remote health monitoring; (4) research challenges and opportunities in medical data analytics; (5) examples of case studies and practical solutions
A cloud-based Analytics-Platform for user-centric Internet of Things domains – Prototype and Performance Evaluation
Data analytics have the potential to increase the value of data emitted from smart devices in user-centric Internet of Things environments, such as smart home, drastically. In order to allow businesses and end-consumers alike to tap into this potential, appropriate analytics architectures must be present. Current solutions in this field do not tackle all of the diverse challenges and requirements, which were identified in previous research. Specifically, personalized, extensible analytics solutions, which still offer the means to address big data problems are scarce. In this paper, we therefore present an architectural solution, which was specifically designed to address the named challenges. Furthermore, we offer insights into the prototypical implementation of the proposed concept as well as an evaluation of its performance against traditional big data architectures
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