GCN-GRU:一种无线传感器网络故障检测模型

GRN-GRU:a fault detection model for wireless sensor networks

无线传感器网络已经成为一种实时监测环境的解决方案并被广泛应用于各种领域。由于网络中的传感器容易受到复杂工作环境和自身硬件等因素影响而发生故障,因此无线传感器网络故障检测在其应用领域中是不可缺少的环节。针对无线传感器网络中的故障检测问题,提出了一种融合图卷积网络和门控循环单元的故障检测模型GCN-GRU,该模型由输入层、时空处理层和输出层组成。输入层接收传感器网络数据和由无线传感器网络构建的图模型并将其传输至时空处理层;在时空处理层中,运用图卷积网络提取无线传感器网络的空间分布特征及故障在高维空间的特征,并将其构造为时序序列的高维数据作为门控循环单元的输入,之后通过门控循环单元对传感器网络数据的时间演变特征和空间演化特征进行提取和融合,最后在输出层得到故障检测结果。为了评估GCN-GRU模型的性能,将其与现有的无线传感器网络故障检测算法进行仿真对比。仿真结果表明,GCN-GRU模型相较于对比算法显著地提高了故障检测率并降低了虚警率,能更有效地识别出故障传感器。

Wireless sensor networks(WSN)have become a real-time environmental monitoring solution and are widely used in various fields.Since the sensors in the network are easily affected by complex working environment,their own hardware and other factors,they may fail to work.Therefore,fault detection in wireless sensor networks is an indispensable link in its application field.To address the problem of fault sensor detection in the wireless sensor network,this paper proposes a fault detection model named GCN-GRU,which hybridizes a graph convolutional network(GCN)and a gate recurrent unit(GRU).The model consists of three layers:input layer,spatiotemporal processing layer and output layer.The input layer receives the sensor network data and the graph model constructed by the WSN and transmits them to the spatiotemporal processing layer.In the spatiotemporal processing layer,the spatial distribution features of the WSN and the characteristics of faults in high-dimensional space are extracted by the GCN,and they are constructed as the high-dimensional data of time series which act as the input of the GRU.Then the temporal evolution features of sensor network data and the temporal and spatial evolution characteristics are extracted and fused by the GRU.Finally,the fault detection results are obtained in the output layer.To evaluate the performance of the GCN-GRU,this paper compares the GCN-GRU model with existing fault detection algorithms for the WSN.Numerical experiments show that the GCN-GRU model can significantly improve the fault detection rate and reduce the false alarm rate,thus effectively identifying faulty sensors compared with the existing algorithms.