Using context and preferences to implement self-adapting pervasive computing applications

Applications that exploit contextual information in order to adapt their behaviour to dynamically changing operating environments and user requirements are increasingly being explored as part of the vision of pervasive or ubiquitous computing. Despite recent advances in infrastructure to support these applications through the acquisition, interpretation and dissemination of context data from sensors, they remain prohibitively difficult to develop and have made little penetration beyond the laboratory. This situation persists largely due to a lack of appropriately high-level abstractions for describing, reasoning about and exploiting context information as a basis for adaptation. In this paper, we present our efforts to address this challenge, focusing on our novel approach involving the use of preference information as a basis for making flexible adaptation decisions. We also discuss our experiences in applying our conceptual and software frameworks for context and preference modelling to a case study involving the development of an adaptive communication application.

Developing context-aware pervasive computing applications: Models and approach

There is growing interest in the use of context-awareness as a technique for developing pervasive computing applications that are flexible, adaptable, and capable of acting autonomously on behalf of users. However, context-awareness introduces a variety of software engineering challenges. In this paper, we address these challenges by proposing a set of conceptual models designed to support the software engineering process, including context modelling techniques, a preference model for representing context-dependent requirements, and two programming models. We also present a software infrastructure and software engineering process that can be used in conjunction with our models. Finally, we discuss a case study that demonstrates the strengths of our models and software engineering approach with respect to a set of software quality metrics.

A software engineering framework for context-aware pervasive computing

There is growing interest in the use of context-awareness as a technique for developing pervasive computing applications that are flexible, adaptable, and capable of acting autonomously on behalf of users. However, context-awareness introduces various software engineering challenges, as well as privacy and usability concerns. In this paper, we present a conceptual framework and software infrastructure that together address known software engineering challenges, and enable further practical exploration of social and usability issues by facilitating the prototyping and fine-tuning of context-aware applications.

Towards a standards-based autonomic context management system

Pervasive computing applications must be sufficiently autonomous to adapt their behaviour to changes in computing resources and user requirements. This capability is known as context-awareness. In some cases, context-aware applications must be implemented as autonomic systems which are capable of dynamically discovering and replacing context sources (sensors) at run-time. Unlike other types of application autonomy, this kind of dynamic reconfiguration has not been sufficiently investigated yet by the research community. However, application-level context models are becoming common, in order to ease programming of context-aware applications and support evolution by decoupling applications from context sources. We can leverage these context models to develop general (i.e., application-independent) solutions for dynamic, run-time discovery of context sources (i.e., context management). This paper presents a model and architecture for a reconfigurable context management system that supports interoperability by building on emerging standards for sensor description and classification.

Active node supporting context-aware vertical handover in pervasive computing environment with redundant positioning

A major requirement for pervasive systems is to integrate context-awareness to support heterogeneous networks and device technologies and at the same time support application adaptations to suit user activities. However, current infrastructures for pervasive systems are based on centralized architectures which are focused on context support for service adaptations in response to changes in the computing environment or user mobility. In this paper, we propose a hierarchical architecture based on active nodes, which maximizes the computational capabilities of various nodes within the pervasive computing environment, while efficiently gathering and evaluating context information from the user's working environment. The migratable active node architecture employs various decision making processes for evaluating a rich set of context information in order to dynamically allocate active nodes in the working environment, perform application adaptations and predict user mobility. The active node also utilizes the Redundant Positioning System to accurately manage user's mobility. This paper demonstrates the active node capabilities through context-aware vertical handover applications.

A review of awareness interventions in brain injury rehabilitation

Unawareness related to brain injury has implications for participation in rehabilitation, functional outcomes, and the emotional well-being of clients. Addressing disorders of awareness is an integral component of many rehabilitation programmes, and a review of the literature identified a range of awareness interventions that include holistic milieu-oriented neuropsychological programmes, psychotherapy, compensatory and facilitatory approaches, structured experiences, direct feedback, videotaped feedback, confrontational techniques, cognitive therapy, group therapy, game formats and behavioural intervention. These approaches are examined in terms of their theoretical bases and research evidence. A distinction is made between intervention approaches for unawareness due to neurocognitive factors and approaches for unawareness due to psychological factors. The socio-cultural context of unawareness is a third factor presented in a biopsychosocial framework to guide clinical decisions about awareness interventions. The ethical and methodological concerns associated with research on awareness interventions are discussed. The main considerations relate to the embedded nature of awareness interventions within rehabilitation programmes, the need for individually tailored interventions, differing responses according to the nature of unawareness, and the risk of eliciting emotional distress in some clients.

Symptom reporting and associations with compensation status, self-awareness, causal attributions, and emotional wellbeing following traumatic brain injury

Individuals seeking compensation following traumatic brain injury (TBI) are often found to report a disproportionately high level of symptoms relative to objective indicators of impairment. Previous studies highlight that level of symptom reporting is also related to self-awareness, causal attribution, and emotional wellbeing. Therefore, the reasons for high symptom reporting in the context of compensation are generally unclear. This study aimed to identify whether self-awareness, causal attribution, and emotional wellbeing are significantly associated with level of symptom reporting after controlling for compensation status. A sample of 54 participants with TBI comprised two groups, namely, claimants (n = 27) and non-claimants (n = 27), who were similar in terms of demographic and neuro-cognitive variables. Participants completed the Symptom Expectancy Checklist, Hospital Anxiety Depression Scale, Awareness Questionnaire and a causal attribution scale. A series of independent t tests and Pearson's correlations identified that a higher level of symptom reporting was associated with the following: seeking compensation, less severe TBI, increased age, greater self-awareness, increased post-injury changes reported by relatives, a higher level of mood symptoms, and a tendency to blame other people. Multivariate analysis identified that after controlling for demographic, injury, and compensation status variables, level of mood symptoms and self-awareness were significantly associated with level of symptom reporting. The findings suggest that mood symptoms and heightened self-awareness are significantly related to high symptom reporting independent of compensation status, thus supporting the need for clinicians to interpret symptom reporting within a biopsychosocial context.