Modeling Spatial Relevancy in Context-Aware Systems Using Fuzzy Intervals

With an ongoing variety of pervasive computing devices integrated in our environment and an increasing mobility of users, it is necessary for mobile systems and services to be context-aware. Introducing relevant contexts to the user is the main properties of context-aware systems especially spatial relevant contexts. Most often as situations change gradually, there is no sharp boundary about how far one can see some relevant objects. It seems that contexts do not have crisp borders where they are true on one side but false on the other side. On the other hand, every context and mobile user has an influence interval in an urban network. So applying fuzzy spatial inervals for contexts and mobile users and defining their spatial relationships could effectively model spatial relevancy parameter. The main contribution of this paper is introducing fuzzy interval algebra for modeling spatial relevancy in context-aware systems. The proposed algorithm is implemented in a context-aware tourist guide system. The experimental results showed that the algorithm could accurately detect the spatial contexts.