Detection and correction method for abnormity data over data streams of sensor networks
Yang Bai · Journal of Qiqihar University · 2009
In this article,a method that is applied to detect and correct abnormity data over data streams of sensor networks is proposed.The temporal denoising method based on wavelet transforms and the spatial data fusion method based on BP neural network are combined in the model.Based on this model,a detection and correction for abnormity data algorithm based on wavelet scale is proposed.The time window used in data fusing is determined by the time threshold in the algorithm.Using the time division and frequency division characters of wavelet transform,detection and correction for abnormity data is related to the sustaining time of abnormity data.The wrong report of abnormity data is gotten rid of by the correcting of the sensor networks abnormity data using the data fusing result of muti-sensors.