Environmental Data Recovery using Polynomial Regression for Large-scale Wireless Sensor Networks

Kohei Ohba, Yoshihiro Yoneda, Koji Kurihara, Takashi Suganuma, Hiroyuki Ito, Noboru Ishihara, Kunihiko Gotoh, Koichiro Yamashita, Kazuya Masu

2016

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.

Paper Citation

in Harvard Style

Ohba K., Yoneda Y., Kurihara K., Suganuma T., Ito H., Ishihara N., Gotoh K., Yamashita K. and Masu K. (2016). Environmental Data Recovery using Polynomial Regression for Large-scale Wireless Sensor Networks . In Proceedings of the 5th International Confererence on Sensor Networks - Volume 1: SENSORNETS, ISBN 978-989-758-169-4, pages 161-168. DOI: 10.5220/0005636901610168

in Bibtex Style

@conference{sensornets16,
author={Kohei Ohba and Yoshihiro Yoneda and Koji Kurihara and Takashi Suganuma and Hiroyuki Ito and Noboru Ishihara and Kunihiko Gotoh and Koichiro Yamashita and Kazuya Masu},
title={Environmental Data Recovery using Polynomial Regression for Large-scale Wireless Sensor Networks},
booktitle={Proceedings of the 5th International Confererence on Sensor Networks - Volume 1: SENSORNETS,},
year={2016},
pages={161-168},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0005636901610168},
isbn={978-989-758-169-4},
}

in EndNote Style

TY - CONF
JO - Proceedings of the 5th International Confererence on Sensor Networks - Volume 1: SENSORNETS,
TI - Environmental Data Recovery using Polynomial Regression for Large-scale Wireless Sensor Networks
SN - 978-989-758-169-4
AU - Ohba K.
AU - Yoneda Y.
AU - Kurihara K.
AU - Suganuma T.
AU - Ito H.
AU - Ishihara N.
AU - Gotoh K.
AU - Yamashita K.
AU - Masu K.
PY - 2016
SP - 161
EP - 168
DO - 10.5220/0005636901610168