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

Hang Yang, Simon Fong, Kyungeun Cho, Junbo Wang

Research output: Contribution to journalArticlepeer-review

6 Scopus citations

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.

Original languageEnglish
Pages (from-to)147-162
Number of pages16
JournalInternational Journal of Sensor Networks
Volume20
Issue number3
DOIs
StatePublished - 2016

Keywords

  • Atmospheric pattern recognition
  • Data stream mining
  • Ubiquitous sensor network

Fingerprint

Dive into the research topics of 'Atmospheric pattern recognition of human activities on ubiquitous sensor network using data stream mining algorithms'. Together they form a unique fingerprint.

Cite this