A Novel Spatiotemporal Correlation Anomaly Detection Method Based on Time-Frequency-Domain Feature Fusion and a Dynamic Graph Neural Network in Wireless Sensor Network
Journal:
arXiv
Published Date:
Feb 25, 2025
Abstract
Attention-based transformers have played an important role in wireless sensor
network (WSN) timing anomaly detection due to their ability to capture
long-term dependencies. However, there are several issues that must be
addressed, such as the fact that their ability to capture long-term
dependencies is not completely reliable, their computational complexity levels
are high, and the spatiotemporal features of WSN timing data are not
sufficiently extracted for detecting the correlation anomalies of multinode WSN
timing data. To address these limitations, this paper proposes a WSN anomaly
detection method that integrates frequency-domain features with dynamic graph
neural networks (GNN) under a designed self-encoder reconstruction framework.
First, the discrete wavelet transform effectively decomposes trend and seasonal
components of time series to solve the poor long-term reliability of
transformers. Second, a frequency-domain attention mechanism is designed to
make full use of the difference between the amplitude distributions of normal
data and anomalous data in this domain. Finally, a multimodal fusion-based
dynamic graph convolutional network (MFDGCN) is designed by combining an
attention mechanism and a graph convolutional network (GCN) to adaptively
extract spatial correlation features. A series of experiments conducted on
public datasets and their results demonstrate that the anomaly detection method
designed in this paper exhibits superior precision and recall than the existing
methods do, with an F1 score of 93.5%, representing an improvement of 2.9% over
that of the existing models.
