Recently, the concept of Internet of Things has widely proliferated to offer advanced connectivity between devices, systems, and services that continuously obtain enormous amounts of data from sensors. Recognizing context from the sensor data plays a crucial role in adding value to the raw sensor data. In this article, we propose a context-aware system through device-oriented modeling for the Internet of Things using modular Bayesian networks based on our previous study. A Bayesian network can handle flexibly the uncertain environments of frequent changes in device configuration, and the proposed system can enable us to adjust to the changing Internet of Things environment, making it more flexible. The main contribution of the article lies in the realization of the modular context-aware system with device oriented modeling of Bayesian networks in smart home and the verification of the usability through a subjective test with 116 people. In addition, we evaluate the performance of the proposed system and show the reduction of time complexity using the real data. Compared to other methods such as decision tree and monolithic Bayesian network, the performance improvement is statistically significant according to t-test.
|Journal||International Journal of Distributed Sensor Networks|
|Publication status||Published - 2017 May|
Bibliographical noteFunding Information:
This work was supported by Institute for Information & Communications Technology Promotion (IITP) grant funded by the Korea government (MSIP) (R0124-16-0002, Emotional Intelligence Technology to Infer Human Emotion and Carry on Dialogue Accordingly).
© The Author(s) 2017.
All Science Journal Classification (ASJC) codes
- Computer Networks and Communications