关键词: 检测特征, 压缩感知算法, 异常事件, 极限学习, 传感器网络

Abstract: In order to improve the effect of abnormal event detection in sensor networks, we proposed a sensor network abnormal event detection model based on compressed sensing algorithm. Firstly, the state information of  sensor network was collected, and the compressed sensing algorithm was used to
sample and reconstruct information, after reducing the abnormal events detection information in the sensor network, we deleted some invalid information. Secondly, we extracted feature from the reconstructed sensor network abnormal events detection information to compose feature vector of sensor n
etwork abnormal events detection. Finally, the sensor network abnormal event detection model was established by using the limit learning machine, the simulation experiment of sensor network abnormal event detection was carried out, and the performance of the model was analyzed. The experimental results show that the compressed sensing algorithm can speed up the detection of abnormal events in sensor networks, and the detection rate of abnormal events in sensor networks is higher than 95%, which is significantly higher than other abnormal event detection models in sensor networks.

Key words: sensor network, abnormal event, compressed sensing algorithm, detection characteristics, limit learning