Data Mining Research in Early Warning Model of Chlorine Gas Monitoring Wireless Sensor Network

Author

Wang Rongxin ; Xiu Debin ; Zhou Yushan ; Liu Congning ; Shi Yunbo

Author_Institution

Higher Educ. Key Lab. for Meas. & Control Technol. & Instrum., Harbin Univ. of Sci. & Technol., Harbin, China

fYear

2014

fDate

18-20 Sept. 2014

Firstpage

711

Lastpage

715

Abstract

Massive historical data are stored in chlorine gas monitoring network. So that prediction algorithm of data mining is used to dig historical data not only can make the redundant data reused, but also can forecast the network trend and improve the network early warning model. The chlorine gas monitoring wireless sensor network based on ZigBee was designed in this paper. Then Fletcher-Reeves algorithm was added to dig historical data in the network, forecast the network trend and improve the early warning model. The predicted concentration of chlorine data were trained by data mining model. The maximum relative error between predicted concentration and measured concentration was 11.08%, and the maximum average error was 7.36%. And it can satisfy actual requirements.

Keywords

Zigbee; chemical engineering computing; computerised monitoring; data mining; wireless sensor networks; Fletcher-Reeves algorithm; ZigBee; chlorine gas monitoring wireless sensor network; data mining prediction algorithm; early warning model; Data mining; Data models; Monitoring; Neural networks; Prediction algorithms; Training; Wireless sensor networks; Fletcher-Reeves algorithm; data mining; wireless sensor network;

fLanguage

English

Publisher

ieee

Conference_Titel

Instrumentation and Measurement, Computer, Communication and Control (IMCCC), 2014 Fourth International Conference on

Conference_Location

Harbin

Print_ISBN

978-1-4799-6574-8

Type

conf

DOI

10.1109/IMCCC.2014.151

Filename

6995121