Authors:
Kohei Ohba
1
;
Yoshihiro Yoneda
1
;
Koji Kurihara
2
;
Takashi Suganuma
1
;
Hiroyuki Ito
1
;
Noboru Ishihara
1
;
Kunihiko Gotoh
1
;
Koichiro Yamashita
2
and
Kazuya Masu
1
Affiliations:
1
Tokyo Institute of Technology, Japan
;
2
Fujitsu Laboratories Ltd., Japan
Keyword(s):
Wireless Sensor Networks, Polynomial Regression, Data Recovery, Environment Monitoring.
Related
Ontology
Subjects/Areas/Topics:
Applications and Uses
;
Data Manipulation
;
Data Quality and Integrity
;
Environment Monitoring
;
Sensor Networks
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. F
urthermore, the polynomial regression can recover arbitrary environmental characteristics.
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