A Novel Spatiotemporal Correlation Anomaly Detection Method Based on Time-Frequency-Domain Feature Fusion and a Dynamic Graph Neural Network in Wireless Sensor Network

2025-02-25

AI Summary

To address unreliable long-term dependency modeling, high computational overhead, and insufficient spatiotemporal correlation-based anomaly detection in multi-node time-series data from wireless sensor networks (WSNs), this paper proposes a self-supervised autoencoding reconstruction framework integrating time-frequency domain features with dynamic graph structures. We innovatively design a time-frequency attention mechanism and introduce a multimodal fusion dynamic graph convolutional network (MFDGCN), which jointly leverages discrete wavelet transform, frequency-domain attention, dynamic graph convolution, and an autoencoder for robust spatiotemporal modeling. Evaluated on public benchmarks, our method achieves an F1-score of 93.5%, outperforming state-of-the-art approaches by 2.9 percentage points, with significant improvements in both anomaly detection precision and recall.

Technology Category

• Machine Learning: Time-Series/Data Streams
• Data Mining & Knowledge Management: Anomaly/Outlier Detection
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems

Application Category

• Graph Algorithms and Modeling for the Web: Representation, reconstruction, and subgraph or motif discovery in Web-related graphs
• Systems and Infrastructure for Web, Mobile and WoT: Energy management for devices in mobile Web and WoT environments
• Web Mining and Content Analysis: Models for Web evolution

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.

Problem

• Improves long-term dependency capture in WSN anomaly detection.
• Reduces computational complexity in spatiotemporal feature extraction.
• Enhances detection of correlation anomalies in multinode WSN data.

Innovation

• Discrete wavelet transform for time series decomposition
• Frequency-domain attention mechanism for anomaly detection
• Multimodal fusion-based dynamic graph convolutional network