PARE: Profile-Applied Reasoning Engine for Context-Aware System

M. Robiul Hoque, M. Humayun Kabir, Hyungyu Seo and Sung-Hyun Yang

International Journal of Distributed Sensor Networks, 2016, vol. 12, issue 7, 5389091

Abstract: 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.

Date: 2016
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Persistent link: https://EconPapers.repec.org/RePEc:sae:intdis:v:12:y:2016:i:7:p:5389091

DOI: 10.1177/155014775389091

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