TE-MSTAD fuses time-frequency features with graph-enhanced topology via RWKV and dual-branch networks to detect WSN anomalies, reporting F1 scores of 92.52% and 93.28% on public and real datasets.
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HiFiNet uses edge LSTM autoencoders followed by graph attention aggregation to identify faults in wireless sensor network data more accurately than prior methods on synthetic datasets derived from Intel Lab and MERRA-2 sources.
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A method for detecting spatio-temporal correlation anomalies of WSN nodes based on topological information enhancement and time-frequency feature extraction
TE-MSTAD fuses time-frequency features with graph-enhanced topology via RWKV and dual-branch networks to detect WSN anomalies, reporting F1 scores of 92.52% and 93.28% on public and real datasets.
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HiFiNet: Hierarchical Fault Identification in Wireless Sensor Networks via Edge-Based Classification and Graph Aggregation
HiFiNet uses edge LSTM autoencoders followed by graph attention aggregation to identify faults in wireless sensor network data more accurately than prior methods on synthetic datasets derived from Intel Lab and MERRA-2 sources.