Research in context-aware computing has produced a number of middleware systems for context management. However, development of ubiquitous context-aware applications is still a challenge because most current middleware systems are still focused on isolated and static context-aware environments. Context-aware environments are inherently dynamic as a result of occasional additions or upgrade of sensors, applications or context inference mechanisms.

Context Management for Distributed and Dynamic Context-Aware Computing proposes a novel architecture for context management based on the concept of context domains, allowing applications to keep context interests across distributed context management systems. The authors describe a distributed middleware that implements the aforementioned concepts, without compromising scalability and efficiency of context access.

In Ambient Intelligence (AmI) systems, reasoning is fundamental for triggering actions or adaptations according to specific situations that may be meaningful and relevant to some applications. However, such reasoning operations may need to evaluate context data collected from distributed sources and stored in different devices, as usually not all context data is readily available to the reasoners within the system.

Decentralized Reasoning in Ambient Intelligence proposes a decentralized reasoning approach for performing rule-based reasoning about context data targeting AmI systems. For this purpose, the authors define a context model assuming context data distributed over two sides: the user side, represented by the users and their mobile devices, and the ambient side, represented by the fixed computational infrastructure and ambient services. They formalize the cooperative reasoning operation — in which two entities cooperate to perform decentralized rule-based reasoning — and define a complete process to perform this operation.

Research in context-aware computing has produced a number of middleware systems for context management. However, development of ubiquitous context-aware applications is still a challenge because most current middleware systems are still focused on isolated and static context-aware environments. Context-aware environments are inherently dynamic as a result of occasional additions or upgrade of sensors, applications or context inference mechanisms.

Context Management for Distributed and Dynamic Context-Aware Computing proposes a novel architecture for context management based on the concept of context domains, allowing applications to keep context interests across distributed context management systems. The authors describe a distributed middleware that implements the aforementioned concepts, without compromising scalability and efficiency of context access.

In Ambient Intelligence (AmI) systems, reasoning is fundamental for triggering actions or adaptations according to specific situations that may be meaningful and relevant to some applications. However, such reasoning operations may need to evaluate context data collected from distributed sources and stored in different devices, as usually not all context data is readily available to the reasoners within the system.

Decentralized Reasoning in Ambient Intelligence proposes a decentralized reasoning approach for performing rule-based reasoning about context data targeting AmI systems. For this purpose, the authors define a context model assuming context data distributed over two sides: the user side, represented by the users and their mobile devices, and the ambient side, represented by the fixed computational infrastructure and ambient services. They formalize the cooperative reasoning operation — in which two entities cooperate to perform decentralized rule-based reasoning — and define a complete process to perform this operation.