Driven by the emergence of mobile and ubiquitous computing there is a growing demand for context-aware applications that can dynamically adapt to their runtime environment. Middleware support for providing such adaptation to mobile applications has been an attractive research and development issue for several years now. However, one of the major challenges to support adaptation is that they can not be always foreseen during the design time. In a ubiquitous computing environment, a number of mobile devices running adaptive applications supported by the instances of the same middleware may come and go in a particular adaptation domain in an unanticipated manner. Moreover, third party services may also appear and disappear with respect to that particular adaptation domain. In componentbased application architectures, with the enhancement of integrating services, mobile applications may benefit from using the components that are provided by other users as well as using third party services. At runtime the application is composed of the components and the services available within the adaptation domain. This thesis provides an approach, including theoretical background, an adaptation middleware, an MDA-based application development methodology and a tool chain, in order to solve such unanticipated adaptation problems.

Context awareness, dynamic reconfiguration at runtime and heterogeneity are key characteristics of future distributed systems, particularly in ubiquitous and mobile computing scenarios. The main contributions of this dissertation are theoretical as well as architectural concepts facilitating information exchange and fusion in heterogeneous and dynamic distributed environments. Our main focus is on bridging the heterogeneity issues and, at the same time, considering uncertain, imprecise and unreliable sensor information in information fusion and reasoning approaches. A domain ontology is used to establish a common vocabulary for the exchanged information. We thereby explicitly support different representations for the same kind of information and provide Inter-Representation Operations that convert between them. Special account is taken of the conversion of associated meta-data that express uncertainty and impreciseness. The Unscented Transformation, for example, is applied to propagate Gaussian normal distributions across highly non-linear Inter-Representation Operations. Uncertain sensor information is fused using the Dempster-Shafer Theory of Evidence as it allows explicit modelling of partial and complete ignorance. We also show how to incorporate the Dempster-Shafer Theory of Evidence into probabilistic reasoning schemes such as Hidden Markov Models in order to be able to consider the uncertainty of sensor information when deriving high-level information from low-level data. For all these concepts we provide architectural support as a guideline for developers of innovative information exchange and fusion infrastructures that are particularly targeted at heterogeneous dynamic environments. Two case studies serve as proof of concept. The first case study focuses on heterogeneous autonomous robots that have to spontaneously form a cooperative team in order to achieve a common goal. The second case study is concerned with an approach for user activity recognition which serves as baseline for a context-aware adaptive application. Both case studies demonstrate the viability and strengths of the proposed solution and emphasize that the Dempster-Shafer Theory of Evidence should be preferred to pure probability theory in applications involving non-linear Inter-Representation Operations.