Title: Atmospheric pattern recognition of human activities on ubiquitous sensor network using data stream mining algorithms

Authors: Hang Yang; Simon Fong; Kyungeun Cho; Junbo Wang

Addresses: Research Institute of Smart Grid, Nos. 6/8, Shui Jun Gang, Est Dongfeng Road, Yue Xiu District, Guangzhou, Guangdong, 510080, China ' Department of Science and Technology, E11-4023, University of Macau, Avenida da Universidade, Taipa, Macau SAR ' Department of Multimedia, Dongguk University 26, Pil-dong 3-ga, Jung-gu Seoul 100-715, Korea ' University Business Innovation Center, University of Aizu, Japan

Abstract: Ubiquitous sensor networks gain tremendous popularity nowadays with practical applications such as detection of natural disasters. These applications collect real-time data about the atmospheric measurements from sensors that are installed in the field. In this paper we argue that traditional data mining methods run short of accurately analysing the activity patterns from the sensor data stream. We evaluate the successor of these algorithms which is known as data stream mining by using an example of an indoor ubiquitous sensor network. They measure various atmospheric values that are supposedly prone to the influences of different human activities. Superior result is shown in the experiment that runs on this empirical data stream. The contribution of this paper is on a comparative study between using traditional and data stream mining algorithms, in a scenario where different atmospheric patterns are to be recognised from streaming sensor data.

Keywords: atmospheric pattern recognition; ubiquitous sensor networks; data stream mining; data mining; atmospheric measurements; indoor networks; human activities; streaming sensor data; model induction; rule extraction; environmental sensing; decision support centres.

DOI: 10.1504/IJSNET.2016.075364

International Journal of Sensor Networks, 2016 Vol.20 No.3, pp.147 - 162

Accepted: 22 Oct 2014
Published online: 17 Mar 2016 *

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