22,716 research outputs found
Context Aware Computing for The Internet of Things: A Survey
As we are moving towards the Internet of Things (IoT), the number of sensors
deployed around the world is growing at a rapid pace. Market research has shown
a significant growth of sensor deployments over the past decade and has
predicted a significant increment of the growth rate in the future. These
sensors continuously generate enormous amounts of data. However, in order to
add value to raw sensor data we need to understand it. Collection, modelling,
reasoning, and distribution of context in relation to sensor data plays
critical role in this challenge. Context-aware computing has proven to be
successful in understanding sensor data. In this paper, we survey context
awareness from an IoT perspective. We present the necessary background by
introducing the IoT paradigm and context-aware fundamentals at the beginning.
Then we provide an in-depth analysis of context life cycle. We evaluate a
subset of projects (50) which represent the majority of research and commercial
solutions proposed in the field of context-aware computing conducted over the
last decade (2001-2011) based on our own taxonomy. Finally, based on our
evaluation, we highlight the lessons to be learnt from the past and some
possible directions for future research. The survey addresses a broad range of
techniques, methods, models, functionalities, systems, applications, and
middleware solutions related to context awareness and IoT. Our goal is not only
to analyse, compare and consolidate past research work but also to appreciate
their findings and discuss their applicability towards the IoT.Comment: IEEE Communications Surveys & Tutorials Journal, 201
Mixed reality participants in smart meeting rooms and smart home enviroments
Humanâcomputer interaction requires modeling of the user. A user profile typically contains preferences, interests, characteristics, and interaction behavior. However, in its multimodal interaction with a smart environment the user displays characteristics that show how the user, not necessarily consciously, verbally and nonverbally provides the smart environment with useful input and feedback. Especially in ambient intelligence environments we encounter situations where the environment supports interaction between the environment, smart objects (e.g., mobile robots, smart furniture) and human participants in the environment. Therefore it is useful for the profile to contain a physical representation of the user obtained by multi-modal capturing techniques. We discuss the modeling and simulation of interacting participants in a virtual meeting room, we discuss how remote meeting participants can take part in meeting activities and they have some observations on translating research results to smart home environments
Smart Photos
Recent technological leaps have been a great catalyst for changing how people interact with the world around us. Specifically, the field of Augmented Reality has led to many software and hardware advances that have formed a digital intermediary between humans and their environment. As of now, Augmented Reality is available to the select few with the means of obtaining Google Glass, Oculus Rifts, and other relatively expensive platforms. Be that as it may, the tech industry\u27s current goal has been integration of this technology into the public\u27s smartphones and everyday devices. One inhibitor of this goal is the difficulty of finding an Augmented Reality application whose usage could satisfy an everyday need or attraction. Augmented reality presents our world in a unique perspective that can be found nowhere else in the natural world. However, visual impact is weak without substance or meaning. The best technology is invisible, and what makes a good product is its ability to fill a void in a person\u27s life. The most important researchers in this field are those who have been augmenting the tasks that most would consider mundane, such as overlaying nutritional information directly onto a meal [4].
In the same vein, we hope to incorporate Augmented Reality into everyday life by unlocking the full potential of a technology often believed to have already have reached its peak. The humble photograph, a classic invention and unwavering enhancement to the human experience, captures moments in space and time and compresses them into a single permanent state. These two-dimensional assortments of pixels give us a physical representation of the memories we form in specific periods of our lives. We believe this representation can be further enhanced in what we like to call a Smart Photo. The idea behind a Smart Photo is to unlock the full potential in the way that people can interact with photographs. This same notion is explored in the field of Virtual Reality with inventions such as 3D movies, which provide a special appeal that ordinary 2D films cannot. The 3D technology places the viewer inside the film\u27s environment. We intend to marry this seemingly mutually exclusive dichotomy by processing 2D photos alongside their 3D counterparts
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State-of-the-art on research and applications of machine learning in the building life cycle
Fueled by big data, powerful and affordable computing resources, and advanced algorithms, machine learning has been explored and applied to buildings research for the past decades and has demonstrated its potential to enhance building performance. This study systematically surveyed how machine learning has been applied at different stages of building life cycle. By conducting a literature search on the Web of Knowledge platform, we found 9579 papers in this field and selected 153 papers for an in-depth review. The number of published papers is increasing year by year, with a focus on building design, operation, and control. However, no study was found using machine learning in building commissioning. There are successful pilot studies on fault detection and diagnosis of HVAC equipment and systems, load prediction, energy baseline estimate, load shape clustering, occupancy prediction, and learning occupant behaviors and energy use patterns. None of the existing studies were adopted broadly by the building industry, due to common challenges including (1) lack of large scale labeled data to train and validate the model, (2) lack of model transferability, which limits a model trained with one data-rich building to be used in another building with limited data, (3) lack of strong justification of costs and benefits of deploying machine learning, and (4) the performance might not be reliable and robust for the stated goals, as the method might work for some buildings but could not be generalized to others. Findings from the study can inform future machine learning research to improve occupant comfort, energy efficiency, demand flexibility, and resilience of buildings, as well as to inspire young researchers in the field to explore multidisciplinary approaches that integrate building science, computing science, data science, and social science
The simplicity project: easing the burden of using complex and heterogeneous ICT devices and services
As of today, to exploit the variety of different "services", users need to configure each of their devices by using different procedures and need to explicitly select among heterogeneous access technologies and protocols. In addition to that, users are authenticated and charged by different means. The lack of implicit human computer interaction, context-awareness and standardisation places an enormous burden of complexity on the shoulders of the final users. The IST-Simplicity project aims at leveraging such problems by: i) automatically creating and customizing a user communication space; ii) adapting services to user terminal characteristics and to users preferences; iii) orchestrating network capabilities. The aim of this paper is to present the technical framework of the IST-Simplicity project. This paper is a thorough analysis and qualitative evaluation of the different technologies, standards and works presented in the literature related to the Simplicity system to be developed
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