Fusion reconstruction mechanism and contrast learning method for WSN abnormal node detection

To tackle the defects of self-supervised learning anomaly detection methods for wireless sensor network (WSN) need to address the problems of single negative sample types and lack of diversity, as well as insufficient extraction of spatiotemporal features from multimodal data of wireless sensor network nodes. To address these challenges, a wireless sensor network anomaly node detection method that combines contrastive learning and reconstruction mechanisms was proposed. Firstly, this method provided sufficient positive and negative example information representation for the reconstruction model by using contrastive learning methods, and combined with generative adversarial network (GAN) to generate negative examples with diverse characteristics. Secondly, a dual layer spatiotemporal feature extraction module based on multi-head attention and graph neural network was designed. Through a series of comparative experiments on actual public datasets and their experimental results, it is shown that the method designed has better accuracy and recall compared to traditional anomaly detection methods and recent graph neural network methods.