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

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

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.

Suggested Citation

  • M. Robiul Hoque & M. Humayun Kabir & Hyungyu Seo & Sung-Hyun Yang, 2016. "PARE: Profile-Applied Reasoning Engine for Context-Aware System," International Journal of Distributed Sensor Networks, , vol. 12(7), pages 5389091-538, July.
    • Handle: RePEc:sae:intdis:v:12:y:2016:i:7:p:5389091
    DOI: 10.1177/155014775389091
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