Towards Well-Founded and Richer Context-Awareness Conceptual Models

We observe that context-aware systems currently developed in one domain or another are mostly technology-driven, and not so much user-centric. They are often not based on a thorough analysis of the effects they produce when interacting with their context, especially regarding the contribution of these effects to user needs. We argue that a conceptual framework is needed to support such analyses. In this paper we identify the concepts necessary to define important structural aspects of a context-aware system and its context, and to formulate generalizations about effects of the interaction of the context-aware system and its context related to user needs. Using this conceptual framework, we classify context-aware systems in terms of the kinds of context assumptions that we can make at design time, and we discuss several threats to validity of a context-aware system. We believe that the proposed conceptual framework can help to better assess the utility concerning a context-aware system design. We use various examples of context-aware applications to illustrate our ideas.</p

Metadata and ontologies for organizing students’ memories and learning: standards and convergence models for context awareness

Este artículo trata de las ontologías que sirven para la comprensión en contexto y la Gestión de la Información Personal (PIM)y su aplicabilidad al proyecto Memex Metadata(M2). M2 es un proyecto de investigación de la Universidad de Carolina del Norte en Chapel Hill para mejorar la memoria digital de los alumnos utilizando tablet PC, la tecnología SenseCam de Microsoft y otras tecnologías móviles(p.ej. un dispositivo de GPS) para capturar el contexto del aprendizaje. Este artículo presenta el proyecto M2, dicute el concepto de los portafolios digitales en las actuales tendencias educativas, relacionándolos con las tecnologías emergentes, revisa las ontologías relevantes y su relación con el proyecto CAF (Context Awareness Framework), y concluye identificando las líneas de investigación 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

The use of social media for improving energy consumption awareness and efficiency: An overview of existing tools

Raising consumers’ awareness of energy consumption is one of the first steps in encouraging the adoption of energy saving behaviours that result in energy efficiency. Green information systems are becoming recognised as a solution to many environmental problems although information technology (e.g. disposal of IT devices) has also been associated with causing detrimental effects on the environment. Researchers and practitioners have begun to focus on Green ICT but there is little scholarly research on the use of ICT tools such as social media from an energy efficiency context to raise consumer awareness and improve their engagement in tackling environmental issues. Therefore, the aim of this paper is to explore the use of social media and existing tools for the interaction of people on energy saving discussions and for generating awareness and engagement (which thereby leads to energy efficiency behaviour). In this paper the authors provide a state of the art review around the concept of energy awareness, models of consumer engagement, and more importantly the use of social media in the energy efficiency context. This research is based on a desk-based normative review and seeks to provide a better understanding to both scholars and practitioners involved in the use of ICT for driving energy consumer awareness and engagement for energy efficiency.This work evolved in the context of the project DAREED (Decision support Advisor for innovative business models and useR engagement for smart Energy Efficient Districts), www.dareed.eu, a project co-funded by the EC within FP7, Grant agreement no: 609082

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

A Consolidated View of Context for Intelligent Systems

This paper's main objective is to consolidate the knowledge on context in the realm of intelligent systems, systems that are aware of their context and can adapt their behavior accordingly. We provide an overview and analysis of 36 context models that are heterogeneous and scattered throughout multiple fields of research. In our analysis, we identify five shared context categories: social context, location, time, physical context, and user context. In addition, we compare the context models with the context elements considered in the discourse on intelligent systems and find that the models do not properly represent the identified set of 3,741 unique context elements. As a result, we propose a consolidation of the findings from the 36 context models and the 3,741 unique context elements. The analysis reveals that there is a long tail of context categories that are considered only sporadically in context models. However, particularly these context elements in the long tail may be necessary for improving intelligent systems' context awareness

A model for context awareness for mobile applications using multiple-input sources

Context-aware computing enables mobile applications to discover and benefit from valuable context information, such as user location, time of day and current activity. However, determining the users’ context throughout their daily activities is one of the main challenges of context-aware computing. With the increasing number of built-in mobile sensors and other input sources, existing context models do not effectively handle context information related to personal user context. The objective of this research was to develop an improved context-aware model to support the context awareness needs of mobile applications. An existing context-aware model was selected as the most complete model to use as a basis for the proposed model to support context awareness in mobile applications. The existing context-aware model was modified to address the shortcomings of existing models in dealing with context information related to personal user context. The proposed model supports four different context dimensions, namely Physical, User Activity, Health and User Preferences. A prototype, called CoPro was developed, based on the proposed model, to demonstrate the effectiveness of the model. Several experiments were designed and conducted to determine if CoPro was effective, reliable and capable. CoPro was considered effective as it produced low-level context as well as inferred context. The reliability of the model was confirmed by evaluating CoPro using Quality of Context (QoC) metrics such as Accuracy, Freshness, Certainty and Completeness. CoPro was also found to be capable of dealing with the limitations of the mobile computing platform such as limited processing power. The research determined that the proposed context-aware model can be used to successfully support context awareness in mobile applications. Design recommendations were proposed and future work will involve converting the CoPro prototype into middleware in the form of an API to provide easier access to context awareness support in mobile applications

Reasoning About Context-Awareness in the Presence of Mobility

Context-awareness is emerging as an important computing paradigm designed to address the special needs of applications that must accommodate or exploit the highly dynamic environments that occur in the presence of physical or logical mobility. A number of formal models are available for reasoning about concurrency. Models designed to capture the specifics of mobility are fewer but still well represented (e.g., Mobile Ambients, π-Calculus, and Mobile UNITY). These models do not, however, provide constructs necessary for explicit modeling of context-aware interactions. This paper builds upon earlier efforts on state-based formal reasoning about mobility and explores the process by which a model such as Mobile UNITY can be transformed to explicitly capture context-awareness. Starting with an ex-amination of the essential features of context-aware systems, this paper explores a range of constructs designed to facilitate a highly decoupled style of programming among context-aware components. The result of this exploration is a model called Context UNITY

Supporting Focus and Context Awareness in 3D Modelling Tasks Using Multi-Layered Displays

Most 3D modelling software have been developed for conventional 2D displays, and as such, lack support for true depth perception. This contributes to making polygonal 3D modelling tasks challenging, particularly when models are complex and consist of a large number of overlapping components (e.g. vertices, edges) and objects (i.e. parts). Research has shown that users of 3D modelling software often encounter a range of difficulties, which collectively can be defined as focus and context awareness problems. These include maintaining position and orientation awarenesses, as well as recognizing distance between individual components and objects in 3D spaces. In this paper, we present five visualization and interaction techniques we have developed for multi-layered displays, to better support focus and context awareness in 3D modelling tasks. The results of a user study we conducted shows that three of these five techniques improve users' 3D modelling task performance