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RW-QAnswer: an assisting system for intelligent environments using semantic technology

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Abstract

We have seen rapid growth of Internet of Things (IoT) paradigm. Challenges of IoT include the need to obtain comprehensive environmental information using multiple and different types of sensors as well as the need to reduce the amount of bandwidth used by a large number of sensors. Especially when image sensors are used, a significant amount of bandwidth is used for sending images. In addition, the use of IoT devices need not be limited to experts who are good at handling sensors. For example, there is the home IoT system which is expected to be used at home by several users. Thus, it is necessary to have an interface which is easy to handle, even for people who are not good at handling data and sensors. To solve these problems, we use Resource Description Framework (RDF) used in the Semantic Web field as metadata of sensor data for comprehensive environmental information acquisition. Then, by linking with an existing RDF search system called QAnswer, which uses natural language, we create a system that enables sensor data search using natural language. Thus, we design an intelligent system which enables users to interact with sensors using natural language. By combining an RDF database and a server which controls the flow of messages, we then investigate the trade-off between the response time to a user’s request and the amount of bandwidth usage by messages. Our results show that in a sensor network using RDF, it is possible to reduce the amount of communication traffic by optimally transferring RDF and sensor data only on arrival of a request and this can be done without much increase in the communication latency.

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Availability of data and material

The datasets during and/or analyzed during the current study available from the corresponding author on reasonable request.

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Authors and Affiliations

  1. Graduate School of Science and Engineering, Aoyama Gakuin University, Sagamihara, Japan

    Koichi Shimoda

  2. The QA Company, Saint-Etienne, France

    Dennis Diefenbach

  3. Université de Lyon, UJM-Saint-Etienne, CNRS, Laboratoire Hubert Curien UMR 5516, F-42023, Saint-Etienne, France

    Kamal Singh & Pierre Maret

  4. Integrated Information Technology, Aoyama Gakuin University, Sagamihara, Japan

    Akihito Taya & Yoshito Tobe

Authors
  1. Koichi Shimoda
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  2. Dennis Diefenbach
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  3. Kamal Singh
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  4. Akihito Taya
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  5. Yoshito Tobe
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  6. Pierre Maret
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Correspondence to Koichi Shimoda.

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Code availability

URL of QAnswer is https://qanswer-frontend.univ-st-etienne.fr it is online and accessible via an API but we cannot make available the codes.

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Shimoda, K., Diefenbach, D., Singh, K. et al. RW-QAnswer: an assisting system for intelligent environments using semantic technology. J Reliable Intell Environ 6, 215–231 (2020). https://doi.org/10.1007/s40860-020-00112-3

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  • DOI: https://doi.org/10.1007/s40860-020-00112-3

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