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.
Madden, Intel lab data, https://db.csail.mit.edu/labdata/ labdata.html, accessed: 2025-05-09 (2004)
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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.