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Context reasoning is an important issue for a context-aware system. Generally, context reasoning is adopted to deduce new context based on the available contexts. The rule-based reasoning is one of the most well-known methods for context reasoning. However, it is difficult for the rule-based algorithm to reason personalized context, because it requires a large number of rules to apply the user's preferences. To address this weakness, in this paper we suggest the Profile-Applied Reasoning Engine (PARE). PARE is an enhanced rule-based reasoning method which uses profiles while reasoning contexts. By using profiles, PARE can become aware of the context that is preferred by a specific individual. To validate the effectiveness of the proposed reasoning engine, we compared the reasoning result of PARE with traditional rule-based reasoning in smart home domain. PARE shows better outcome for reasoning the personalized contexts than the traditional rule-based reasoning. In addition, by using profiles, a significant number of rules have been omitted and consequently the running time is also decreased. Moreover, PARE occupies less memory space which is restricted with number of variables of a rule. Therefore, PARE optimizes both runtime and memory space, which is valuable when making embedded context-aware system.

A smart home provides automated services based on the context of the home environment and user activity. Context acquiring, processing, reasoning, and disseminating to the services are complex tasks for a context-aware system. An appropriate middleware architecture could handle such complexity. In this paper, we proposed a middleware architecture for a context-aware system in smart home environment. Here, the context is modeled based on ontology using Web Ontology Language (OWL). In addition, a profile applied improved rule-based reasoning algorithm is integrated into this middleware to infer high-level contexts from available low-level contexts. Experimental result shows that the middleware provides more accurate and faster reasoning outcome compare with the traditional rule-based reasoning method. Moreover, context-aware service is also selected using the rule-based algorithm, where the service can be extended easily by adding new service rules in the service rule base.

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