In pervasive computing environments, various entities often have to cooperate and integrate seamlessly in a which can, thus, be considered as an amalgamation of the context of several entities interacting and coordinating with each other, and often performing one or more activities. However, none of the existing context models and ontologies address situation modeling. In this paper, we describe the design, structure and implementation of a generic, flexible and extensible context ontology called Rover Context Model Ontology (RoCoMO) for context and situation modeling in pervasive computing systems and environments. We highlight several limitations of the existing context models and ontologies, such as lack of provision for provenance, traceability, quality of context, multiple representation of contextual information, as well as support for security, privacy and interoperability, and explain how we are addressing these limitations in our approach. We also illustrate the applicability and utility of RoCoMO using a practical and extensive case study.
Several surveys such as those by ( @cite_10 , @cite_1 and @cite_13 ) have asserted that, of all the current ontologies used for context modeling, SOUPA is the most comprehensive ontology. However, both SOUPA and CoBrA-Ont have no provision for provenance, quality of context and multiple representations. CONON enables provenance by using the concept of sensed, derived, aggregated or deduced context but lacks features like comparability. Gaia takes on the challenge of modeling uncertainty and reasoning over it. However, their ontologies are restricted to the smart spaces domain. They do not model provenance either. A more detailed evaluation of all these ontologies can be found in the surveys mentioned.
@cite_1 @cite_10 @cite_13
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Ontologies are a widely accepted tool for the modeling of context information. We view the identification of the benefits and challenges of ontologybased models to be an important next step to further improve the usability of ontologies in context-aware applications. We outline a set of criteria with respect to ontology engineering and context modeling and discuss some recent achievements in the area of ontology-based context modeling in order to determine the important next steps necessary to fully exploit ontologies in pervasive computing. Development of context-aware applications is inherently complex. These applications adapt to changing context information: physical context, computational context, and user context tasks. Context information is gathered from a variety of sources that differ in the quality of information they produce and that are often failure prone. The pervasive computing community increasingly understands that developing context-aware applications should be supported by adequate context information modelling and reasoning techniques. These techniques reduce the complexity of context-aware applications and improve their maintainability and evolvability. In this paper we discuss the requirements that context modelling and reasoning techniques should meet, including the modelling of a variety of context information types and their relationships, of situations as abstractions of context information facts, of histories of context information, and of uncertainty of context information. This discussion is followed by a description and comparison of current context modelling and reasoning techniques.