关键词: 无线传感网, 时域特征提取, 围栏入侵, BP神经网络, 模式分类

Abstract: Focused on the issue of behavior classification in the field of security application based on wireless sensor networks, an electronic fence intrusion detection and abnormal pattern classification system is proposed using time domain feature extraction. The method of frequency?domain’s feature extraction contains massive computation with expensive complexity, and the sensors’ sampling rate is high. In order to reduce the system’s transmission burden and time delay, firstly, the raw data is preprocessed to extract time domain features. Then a three-layer BP neural networks classifier is used to classify the target events. Lastly, the accuracy rate of several kinds of typical classifiers are compared. Simulation results indicate that, compared with the method of feature extraction in frequency domain, this method is low in complexity and easy to implement, and the accuracy rate can reach more than 86%. What’s more, for the BP neural networks, the accuracy deviation between the training and testing set is relatively small, while the accuracy is reaching 94% for the testing data set which is higher than others.

Key words: wireless sensor networks, time domain feature extraction, fence intrusion, BP neural networks, pattern classification