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Vol. 199, Issue 4, April 2016, pp. 1-9




Polynomial Regression Techniques for Environmental Data Recovery
in Wireless Sensor Networks

1 Kohei Ohba, 1 Yoshihiro Yoneda, 2 Koji Kurihara, 1 Takashi Suganuma, 1 Hiroyuki Ito, 1 Noboru Ishihara, 1 Kunihiko Gotoh,

2 Koichiro Yamashita and 1 Kazuya Masu

1 Tokyo Institute of Technology, Nagatsutacho 4259, Midori-ku, Kanagawa, 2268503, Japan
2 Network Systems Laboratory, Fujitsu Laboratories Ltd., Kamikodanaka 411, Kawasaki Nakahara-ku, Kanagawa, 2118588, Japan
1 Tel.: 045-924-5031, fax: 045-924-5166

E-mail: paper@lsi.pi.titech.ac.jp


Received: 28 February 2016 /Accepted: 5 April 2016 /Published: 30 April 2016

Digital Sensors and Sensor Sysstems


Abstract: In the near feature, large-scale wireless sensor networks will play an important role in our lives by monitoring our environment with large numbers of sensors. However, data loss owing to data collision between the sensor nodes and electromagnetic noise need to be addressed. As the interval of aggregate data is not fixed, digital signal processing is not possible and noise degrades the data accuracy. To overcome these problems, we have researched an environmental data recovery technique using polynomial regression based on the correlations among environmental data. The reliability of the recovered data is discussed in the time, space and frequency domains. The relation between the accuracy of the recovered characteristics and the polynomial regression order is clarified. The effects of noise, data loss and number of sensor nodes are quantified. Clearly, polynomial regression offers the advantage of low-pass filtering and enhances the signal-to-noise ratio of the environmental data. Furthermore, the polynomial regression can recover arbitrary environmental characteristics. Measured temperature and accelerator characteristics were recovered successfully.


Keywords: Wireless sensor networks, Data loss, Polynomial regression, Data recovery, Environment monitoring.


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