Polynomial Regression Techniques for Environmental Data Recovery in Wireless Sensor Networks

• Published in: Sensors & transducers Vol. 199; no. 4; p. 1
• Main Authors: Ohba, Kohei, Yoneda, Yoshihiro, Kurihara, Koji, Suganuma, Takashi, Ito, Hiroyuki, Ishihara, Noboru, Gotoh, Kunihiko, Yamashita, Koichiro, Masu, Kazuya
• Format: Journal Article
• Language: English
• Published: Toronto IFSA Publishing, S.L 01.04.2016
• Subjects: Accuracy; Back up systems; Data loss; Data recovery; Environment monitoring; Noise; Polynomial regression; Polynomials; Regression; Remote sensors; Sensors; Signal processing; Wavelet transforms; Wireless networks; Wireless sensor networks
• ISSN: 2306-8515; 1726-5479

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, the authors 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.