Comparison of Context-Aware systems (Ontology-based)

There is a growing use of context-awareness techniques for developing pervasive computing applications. These methods are flexible, adaptable, and capable of acting autonomously on behalf of the users. The overall goal is to develop evolvable context-aware applications. A good context information modeling formalism reduces the complexity of context-aware applications and improves their maintainability and evolvability. There are several contexts modeling approaches. However they should be supported by the adequate software engineering methods. The ontology-based models of context information are typically OWL-DL or some of its variations. This ontology web language is becoming a de facto standard in various application domains, and it is supported by a number of reasoning services. In this paper, we investigate different approaches to context modeling and reasoning basing upon OWL (ontology-based) such as: CONON, GAIA, COBRA-ONT, SOUPA and GAS.

Keywords:

Pervasive Computing , Context-aware , Ontology-based Models , OWL Language , CONON , COBRA-ONT , GAIA , SOUPA , GAS