A Comparative Study on Context Modeling Approaches

Context is any information that can be used to characterize the situation of an entity. An entity is a person, place, or object that is considered relevant to the interaction between a user and an application, including the user and applications themselves. Context-awareness is one of the drivers of the pervasive computing paradigm, whereas a well designed model is a key component to the context in any context-aware system. There is an inherent gap between the real-world and the world that can be perceived by computer systems, yielding uncertainty and ambiguity in system perceived context, with consequent effect on the performance of context-aware systems. A primary goal of context models is to enable context-awareness by performing some type of reasoning. Modelling context can be interpreted as a process of building a representation that supposedly embodies occurrences of real-life situations that can be reasoned about. The pervasive computing community increasingly understands that developing context-aware applications should be supported by adequate context information modeling techniques. These techniques reduce the complexity of context-aware applications. In this paper the requirements of context modeling is discussed. It is provides discussion on the most relevant current approaches to modeling context for pervasive computing. This discussion is followed by a comparison of current context modeling techniques.

Keywords:

Context , Context Awareness , Modeling , Pervasive Computing