Environmental Data Recovery Techniques and its Applications using Polynomial Regression in the Sensor Network Systems

• Material Type: 図書
• Title: Environmental Data Recovery Techniques and its Applications using Polynomial Regression in the Sensor Network Systems
• Author/Editor: 石原, 昇; Ishihara, Noboru; 大場, 康平; Ohba, Kouhei; 米田, 嘉浩; Yoneda, Yoshihiro; 栗原, 康志; Kurihara, Koji; 菅沼, 隆史; suganuma, takashi; 伊藤, 浩之; Ito, Hiroyuki; 後藤, 邦彦; Gotoh, Kunihiko; 山下, 浩一郎; Yamashita, Koichiro; 益, 一哉; Masu, Kazuya
• Author Heading: 石原, 昇; Ishihara, Noboru; 大場, 康平; Ohba, Kouhei; 米田, 嘉浩; Yoneda, Yoshihiro; 栗原, 康志; Kurihara, Koji; 菅沼, 隆史; suganuma, takashi; 伊藤, 浩之; Ito, Hiroyuki; 後藤, 邦彦; Gotoh, Kunihiko; 山下, 浩一郎; Yamashita, Koichiro; 益, 一哉; Masu, Kazuya
• Publication Date: 2017-03
• Publication Date (W3CDTF): 2017-03
• Periodical title: Sensors and Applications in Measuring and Automation Control Systems, Book Series: Advances in Sensors: Reviews, Vol. 4
• No. or year of volume/issue: 4 no. Chapter 14
• Volume: 4
• Issue: no. Chapter 14
• Pages: 277-294
• Text Language Code: eng
• Subject Heading: Environmental Data Recovery, Polynomial Regression, Sensor Network Systems
• Target Audience: 一般
• Note (General): In this chapter, polynomial regression for environmental data recovery based on the correlations among the environmental data is applied. Environmental characteristics are recovered from aggregated data of the sensor nodes using polynomial regression. Thus, data loss is tolerated, and the data can be analyzed easily. Basic sinusoidal environmental variations are assumed to evaluate the data recovery functionwith polynomial regression. If the sinusoidal characteristics can be modeled appropriately, arbitrary waveform characteristics, such as single-shot, periodic and non-periodic waveforms, can also be modelled theoretically. The recovered data accuracy is evaluated by comparing the recovered and source characteristics. It is also proposed a data reliability evaluation flow that does not rely on signal analysis. It is clarified the relation between the accuracy of the recovered characteristics and the polynomial regression order, and the effects of data loss and number of sensor nodes is analyzed. Furthermore, it is shown that the use of polynomial regression has the advantage of low-pass filtering that enhances the signal-to-noise ratio (SNR) of the environmental characteristics. In addition, it is shown that polynomial regression can recover arbitrary environmental characteristics.; identifier:oai:t2r2.star.titech.ac.jp:50362318
• Data Provider (Database): 国立情報学研究所 : 学術機関リポジトリデータベース（IRDB）（機関リポジトリ）; https://irdb.nii.ac.jp
• Original Data Provider (Database): 東京工業大学 : 東京工業大学リサーチリポジトリ