arXiv Machine Learning By Saeid Jamshidi, Omar Abdul Wahab, Rolando Herrero, Foutse Khomh

Securing Time Integrity in Energy IoT Against Clock Drift and Y2K38 Failures

Read the original on arXiv Machine Learning →

arXiv:2601. 23147v2 Announce Type: replace Abstract: The integrity of time in distributed Internet of Things (IoT) devices is crucial for reliable operation in energy cyber-physical systems, such as smart grids and microgrids.

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arXiv AI
Aug 25

HiFiNet: Hierarchical Fault Identification in Wireless Sensor Networks via Edge-Based Classification and Graph Aggregation

HiFiNet is a hierarchical fault identification framework for Wireless Sensor Networks that uses edge-based LSTM stacked autoencoders for initial temporal feature extraction and a Graph Attention Network to aggregate neighboring node information for refined classification. The approach captures both local temporal patterns and network-wide spatial dependencies, leading to higher accuracy, F1-score, and precision compared to existing methods. Experiments on synthetic datasets derived from the Intel Lab Dataset and NASA's MERRA-2 reanalysis data demonstrate HiFiNet’s robustness and its ability to balance diagnostic performance with energy efficiency.

By Nguyen Tri Nghia, Nguyen Van Son, Nguyen Thi Hanh