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.
Design and implementation of a cloud -enabled random neural network-based decentralized smart controller with intelligent sensor nodes for HVAC
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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.