An approach to cross-domain situation-based context management and highly adaptive services in pervasive environments

The concept of context-awareness is widely used in mobile and pervasive computing to reduce explicit user input and customization through the increased use of implicit input. It is considered to be the corner stone technique for developing pervasive computing applications that are flexible, adaptable, and capable of acting autonomously on behalf of the user. This requires the applications to take advantage of the context in order to infer the user's objective and relevant environmental features. However, context-awareness introduces various software engineering challenges such as the need to provide developers with middleware infrastructure to acquire the context information available in distributed domains, reasoning about contextual situations that span one or more domains, and providing tools to facilitate building context-aware adaptive services. The separation of concerns is a promising approach in the design of such applications where the core logic is designed and implemented separately from the context handling and adaptation logics. In this respect, the aim of this dissertation is to introduce a unified approach for developing such applications and software infrastructure for efficient context management that together address these software engineering challenges and facilitate the design and implementation tasks associated with such context-aware services. The approach is based around a set of new conceptual foundations, including a context modelling technique that describes context at different levels of abstraction, domain-based context management middleware architecture, cross-domain contextual situation recognition, and a generative mechanism for context-aware service adaptation. Prototype tool has been built as an implementation of the proposed unified approach. Case studies have been done to illustrate and evaluate the approach, in terms of its effectiveness and applicability in real-life application scenarios to provide users with personalized services.