Classification and Anomaly Detection of RFID Data Based on Deep Learning

This research zeroes in on the processing of RFID data and puts forward a deep learning-based approach to achieve classification and anomaly detection. The core algorithm is the Convolutional Neural Network (CNN). Its convolutional layers conduct sliding convolutions on RFID tag data with diverse convolutional kernels to extract local features. The pooling layers perform downsampling to reduce dimensions while retaining crucial features, and the fully connected layers integrate the features to accomplish classification. During the data preprocessing stage, data augmentation techniques such as random rotation, translation, and scaling are employed to enrich the data diversity, expand the dataset scale, and enhance the model's generalization ability so as to adapt to various RFID data. For anomaly detection, an adaptive threshold algorithm is introduced. Based on the distribution characteristics of data and the output of the model, statistical quantities like the mean and standard deviation are dynamically calculated. In combination with the trend of data changes, the threshold is determined, and abnormal labels are discriminated accordingly. Data exceeding the threshold will be marked and processed. Experiments demonstrate that this method achieves remarkable classification accuracy and anomaly detection efficiency on multiple types of RFID datasets, highlighting the potential of deep learning in RFID applications.