115,325 research outputs found
Development of ambient intelligence systems based on collaborative task models
So far, the Ambient Intelligence (AmI) paradigm has been applied to the
development of a great variety of real systems. They use advanced technologies such as
ubiquitous computing, natural interaction and active spaces, which become part of social
environments. In the design of AmI systems, the inherent collaboration among users (with the
purpose of achieving common goals) is usually represented and treated in an ad-hoc manner.
However, the development of this kind of systems can take advantage of rich design models
which embrace concepts in the domain of collaborative systems in order to provide the
adequate support for explicit or implicit collaboration. Thereby, relevant requirements to be
satisfied, such as an effective coordination of human activities by means of task scheduling,
demand to dynamically manage and provide group- and context-awareness information. This
paper addresses the integration of both proactive and collaborative aspects into a unique design
model for the development of AmI systems; in particular, the proposal has been applied to a
learning system. Furthermore, the implementation of this system is based on a blackboardbased
architecture, which provides a well-defined high-level interface to the physical layer.This research is partially supported by a Spanish R&D Project TIN2004-03140,
Ubiquitous Collaborative Adaptive Training (U-CAT)
FutureWare: Designing a Middleware for Anticipatory Mobile Computing
Ubiquitous computing is moving from context-awareness to context-prediction. In order to build truly anticipatory systems
developers have to deal with many challenges, from multimodal sensing to modeling context from sensed data, and, when necessary,
coordinating multiple predictive models across devices. Novel expressive programming interfaces and paradigms are needed for this
new class of mobile and ubiquitous applications.
In this paper we present FutureWare, a middleware for seamless development of mobile applications that rely on context prediction.
FutureWare exposes an expressive API to lift the burden of mobile sensing, individual and group behavior modeling, and future context
querying, from an application developer. We implement FutureWare as an Android library, and through a scenario-based testing and a
demo app we show that it represents an efficient way of supporting anticipatory applications, reducing the necessary coding effort by
two orders of magnitude
Multi-Sensor Context-Awareness in Mobile Devices and Smart Artefacts
The use of context in mobile devices is receiving increasing attention in mobile and ubiquitous computing research. In this article we consider how to augment mobile devices with awareness of their environment and situation as context. Most work to date has been based on integration of generic context sensors, in particular for location and visual context. We propose a different approach based on integration of multiple diverse sensors for awareness of situational context that can not be inferred from location, and targeted at mobile device platforms that typically do not permit processing of visual context. We have investigated multi-sensor context-awareness in a series of projects, and report experience from development of a number of device prototypes. These include development of an awareness module for augmentation of a mobile phone, of the Mediacup exemplifying context-enabled everyday artifacts, and of the Smart-Its platform for aware mobile devices. The prototypes have been explored in various applications to validate the multi-sensor approach to awareness, and to develop new perspectives of how embedded context-awareness can be applied in mobile and ubiquitous computing
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
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Context-awareness for mobile sensing: a survey and future directions
The evolution of smartphones together with increasing computational power have empowered developers to create innovative context-aware applications for recognizing user related social and cognitive activities in any situation and at any location. The existence and awareness of the context provides the capability of being conscious of physical environments or situations around mobile device users. This allows network services to respond proactively and intelligently based on such awareness. The key idea behind context-aware applications is to encourage users to collect, analyze and share local sensory knowledge in the purpose for a large scale community use by creating a smart network. The desired network is capable of making autonomous logical decisions to actuate environmental objects, and also assist individuals. However, many open challenges remain, which are mostly arisen due to the middleware services provided in mobile devices have limited resources in terms of power, memory and bandwidth. Thus, it becomes critically important to study how the drawbacks can be elaborated and resolved, and at the same time better understand the opportunities for the research community to contribute to the context-awareness. To this end, this paper surveys the literature over the period of 1991-2014 from the emerging concepts to applications of context-awareness in mobile platforms by providing up-to-date research and future research directions. Moreover, it points out the challenges faced in this regard and enlighten them by proposing possible solutions
Metadata and ontologies for organizing students’ memories and learning: standards and convergence models for context awareness
Este artĂculo trata de las ontologĂas que sirven para la comprensiĂłn en contexto y la GestiĂłn de la InformaciĂłn Personal (PIM)y su aplicabilidad al proyecto Memex Metadata(M2). M2 es un proyecto de investigaciĂłn de la Universidad de Carolina del Norte en Chapel Hill para mejorar la memoria digital de los alumnos utilizando tablet PC, la tecnologĂa SenseCam de Microsoft y otras tecnologĂas mĂłviles(p.ej. un dispositivo de GPS) para capturar el contexto del aprendizaje. Este artĂculo presenta el proyecto M2, dicute el concepto de los portafolios digitales en las actuales tendencias educativas, relacionándolos con las tecnologĂas emergentes, revisa las ontologĂas relevantes y su relaciĂłn con el proyecto CAF (Context Awareness Framework), y concluye identificando las lĂneas de investigaciĂłn futuras.This paper focuses on ontologies supporting context awareness and Personal Information Management (PIM) and their
applicability in Memex Metadata (M2) project. M2 is a research project of the University of North Carolina at Chapel Hill to
improve student digital memories using the tablet PC, Microsoft’s SenseCam technology, and other mobile technologies (e.g.,
a GPS device) to capture context. The M2 project offers new opportunities studying students’ learning with digital
technologies. This paper introduces the M2 project; discusses E-portfolios and current educational trends related to pervasive
computing; reviews relevant ontologies and their relationship to the projects’ CAF (context awareness framework), and
concludes by identifying future research directions
A User-Focused Reference Model for Wireless Systems Beyond 3G
This whitepaper describes a proposal from Working Group 1, the Human Perspective of the Wireless World, for a user-focused reference model for systems beyond 3G. The general structure of the proposed model involves two "planes": the Value Plane and the Capability Plane. The characteristics of these planes are discussed in detail and an example application of the model to a specific scenario for the wireless world is provided
Quality assessment technique for ubiquitous software and middleware
The new paradigm of computing or information systems is ubiquitous computing systems. The technology-oriented issues of ubiquitous computing systems have made researchers pay much attention to the feasibility study of the technologies rather than building quality assurance indices or guidelines. In this context, measuring quality is the key to developing high-quality ubiquitous computing products. For this reason, various quality models have been defined, adopted and enhanced over the years, for example, the need for one recognised standard quality model (ISO/IEC 9126) is the result of a consensus for a software quality model on three levels: characteristics, sub-characteristics, and metrics. However, it is very much unlikely that this scheme will be directly applicable to ubiquitous computing environments which are considerably different to conventional software, trailing a big concern which is being given to reformulate existing methods, and especially to elaborate new assessment techniques for ubiquitous computing environments. This paper selects appropriate quality characteristics for the ubiquitous computing environment, which can be used as the quality target for both ubiquitous computing product evaluation processes ad development processes. Further, each of the quality characteristics has been expanded with evaluation questions and metrics, in some cases with measures. In addition, this quality model has been applied to the industrial setting of the ubiquitous computing environment. These have revealed that while the approach was sound, there are some parts to be more developed in the future
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