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.
各種検索
サポート
ヘルプ