Abstract
Smart home IoT technologies have provided a new level of overall degrees of control freedom over modern homes. The core of such a system is an edge device. In this paper, a CNN-based LSTM-Autoencoder method is presented to detect anomaly points in five critical operating parameters of an edge device while managing its perpetual operation. This proposed method is based on a hybrid model using 1D-CNN layers in the encoder layer and LSTM layers in the decoder layer. Experiments were conducted using real data from Raspberry Pi devices. Compared to other state-of-the-art methods, the proposed approach had a remarkable accuracy close to 0.996 and an execution time of 312 ms.
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Acknowledgements
This work is partially supported by the PRECEPT project funded by the European Union’s Horizon 2020 under Grant Agreement No. 958284 and the SMART2B project funded by the European Union’s Horizon 2020 under Grant Agreement 101023666.
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Papaioannou, A. et al. (2023). Self-protection of IoT Gateways Against Breakdowns and Failures Enabling Automated Sensing and Control. In: Maglogiannis, I., Iliadis, L., Papaleonidas, A., Chochliouros, I. (eds) Artificial Intelligence Applications and Innovations. AIAI 2023 IFIP WG 12.5 International Workshops. AIAI 2023. IFIP Advances in Information and Communication Technology, vol 677. Springer, Cham. https://doi.org/10.1007/978-3-031-34171-7_18
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