To address the issue that existing wireless sensor network (WSN)-based anomaly detection methods only consider and analyze temporal features, in this article, a self-supervised learning-based anomalous node detection method based on an autoencoder is designed. This method integrates the extraction of temporal WSN data flow feature, spatial position feature, and intermodal WSN correlation feature into the autoencoder design to make full use of the spatial and temporal information of the WSN for anomaly detection. First, a fully connected network is used to extract the temporal features of nodes by considering a single mode from a local spatial perspective. Second, a graph neural network is used to introduce the WSN topology from a global spatial perspective for anomaly detection and to extract the spatial and temporal features of the data flows of nodes and their neighbors by considering a single mode. Then, an adaptive fusion method involving weighted summation is used to extract the relevant features between different modes. In addition, the gated recurrent unit network structure is introduced to solve the long-term dependence problem in the time dimension. Eventually, the reconstructed output of the decoder and the hidden layer representation of the autoencoder are fed into a fully connected network to calculate the anomaly probability for the current system. Since the spatial feature extraction operation is performed first, the designed method can be applied for anomaly detection in large-scale networks with the addition of a clustering operation. Experiments show that the designed method outperforms baseline methods, and the F1 score reaches 90.6%, at least 5.2% higher than those of existing anomaly detection methods based on unsupervised reconstruction and prediction.

Anomaly detection is a critical technique that ensures the reliability of WSNs. However, most existing anomaly detection methods only consider the case of single modal data flow anomaly detection for each node or multiple modal time series data flow anomaly detection for a single node and do not consider the case of multiple nodes and multiple time series data flow simultaneously,and it limited the ability of anomaly detection. In this paper, a novel anomaly detection model is proposed for multimodal WSN data flows. First, the temporal features and modal correlation features extracted from each sensor node are fused into one vector representation, then it is further aggregated with the spatial features represented the spatial position relationship of the nodes; finally,the current time-series data of WSN nodes are predicted, and abnormal states are identified according to the fusion features. The simulation results obtained on a public dataset show that the proposed approach can significantly improve upon existing methods interms of robustness, and its F1 score reaches 0.90, which is 14.2% higher than that of the graph convolution network (GCN) with longshort-term memory (LSTM).

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. Code is publicly available at https://github.com/GuetYe/anomaly_detection.

To address the challenges of obtaining network state information, flexibly forwarding data, and improving the communication quality of service (QoS) in wireless network transmission environments in response to dynamic changes in network topology, this paper introduces an intelligent routing algorithm based on deep reinforcement learning (DRL) with network situational awareness under a software-defined wireless networking (SDWN) architecture. First, comprehensive network traffic information is collected under the SDWN architecture, and a graph convolutional network-gated recurrent unit (GCN-GRU) prediction mechanism is used to perceive future traffic trends. Second, a proximal policy optimization (PPO) DRL-based data forwarding mechanism is designed in the knowledge plane. The predicted network traffic matrix and topology information matrix are treated as the DRL environment, while next-hop adjacent nodes are treated as executable actions, and action selection policies are designed for different network conditions. To guide the learning and improvement of the DRL agent’s routing strategy, reward functions of different forms are designed by utilizing network link information and different penalty mechanisms. Additionally, importance sampling steps and gradient clipping methods are employed during gradient updating to enhance the convergence speed and stability of the designed intelligent routing method. Experimental results show that this solution outperforms traditional routing methods in network throughput, delay, packet loss rate, and wireless node distance. Compared to value-function-based Dueling Deep Q-Network (DQN) routing, the convergence of the proposed method is significantly faster and more stable. Simultaneously, hardware storage consumption is reduced, and real-time routing decisions can be made using the current network state information. The source code can be accessed at https://github.com/GuetYe/DRL-PPONSA.

Wireless sensor networks (WSNs) use distributed nodes for tasks such as environmental monitoring and surveillance. The existing anomaly detection methods fail to fully capture correlations in multi-node, multi-modal time series data, limiting their effectiveness. Additionally, they struggle with small sample scenarios because they do not effectively map features to classes. To address these challenges, this paper presents an anomaly detection approach that integrates deep learning with metric learning. A framework incorporating a graph attention network (GAT) and a Transformer is developed to capture spatial and temporal features. A novel distance measurement module improves similarity learning by considering both intra-class and inter-class relationships. Joint metric-classification training improves model accuracy and generalization. Experiments conducted on public datasets demonstrate that the proposed approach achieves an F1 score of 0.89, outperforming the existing approaches by 7%.