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http://dx.doi.org/10.6109/jkiice.2022.26.1.7

Improved Sensor Filtering Method for Sensor Registry System  

Chen, Haotian (Department of Software Convergence Engineering, Kunsan National University)
Jung, Hyunjun (Department of Software Convergence Engineering, Kunsan National University)
Lee, Sukhoon (Department of Software Convergence Engineering, Kunsan National University)
On, Byung-Won (Department of Software Convergence Engineering, Kunsan National University)
Jeong, Dongwon (Department of Software Convergence Engineering, Kunsan National University)
Publication Information
Journal of the Korea Institute of Information and Communication Engineering / v.26, no.1, 2022 , pp. 7-14 More about this Journal
Abstract
Sensor Registry System (SRS) has been devised for maintaining semantic interoperability of data on heterogeneous sensor networks. SRS measures the connectability of the mobile device to ambient sensors based on positions and only provides metadata of sensors that may be successfully connected. The step of identifying the ambient sensors which can be successfully connected is called sensor filtering. Improving the performance of sensor filtering is one of the core issues of SRS research. In reality, GPS sometimes shows the wrong position and thus leads to failed sensor filtering. Therefore, this paper proposes a new sensor filtering strategy using geographical embedding and neural network-based path prediction. This paper also evaluates the service provision rate with the Monte Carlo approach. The empirical study shows that the proposed method can compensate for position abnormalities and is an effective model for sensor filtering in SRS.
Keywords
Geographical embedding; Neural network; Path prediction; Sensor filtering; Sensor registry system;
Citations & Related Records
Times Cited By KSCI : 3  (Citation Analysis)
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