摘要
为改善传感器网络异常事件检测效果,提出一种基于压缩感知算法的传感器网络异常事件检测模型.首先采集传感器网络状态信息,并采用压缩感知算法对信息进行采样和重构,在减少传感器网络异常事件检测信息的同时,删除一些无效信息;然后从重构后的传感器网络异常事件检测信息中提取特征,组成传感器网络异常事件检测的特征向量;最后采用极限学习机建立传感器网络异常事件检测模型,并进行传感器网络异常事件检测仿真实验,分析模型的性能.实验结果表明,压缩感知算法可加快传感器网络异常事件检测速度,且传感器网络异常事件检测率高于95%,明显高于其他传感器网络异常事件检测模型.
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 network 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.
出处
《吉林大学学报（理学版）》
CAS
CSCD
北大核心
2018年第2期375-381,共7页
Journal of Jilin University:Science Edition
基金
江苏省自然科学基金青年基金(批准号:BK20150432)
关键词
异常事件
压缩感知算法
传感器网络
极限学习
检测特征
abnormal event
compressed sensing algorithm
sensor network
limit learning
detection characteristics
