  Abnormal data detection is an important step to ensure the accuracy and
reliability of node data in wireless sensor networks. In this paper, a data
classification method based on convolutional neural network is proposed to
solve the problem of data anomaly detection in wireless sensor networks. First,
Normal data and abnormal data generated after injection fault are normalized
and mapped to gray image as input data of the convolutional neural network.
Then, based on the classical convolution neural network, three new
convolutional neural network models are designed by designing the parameters of
the convolutional layer and the fully connected layer. This model solves the
problem that the performance of traditional detection algorithm is easily
affected by relevant threshold through self-learning data characteristics of
convolution layer. The experimental results show that this method has better
detection performance and higher reliability.
