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.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Marikyan, D., Papagiannidis, S., Alamanos, E.: A systematic review of the smart home literature: a user perspective. Technol. Forecast. Soc. Chang. 138, 139–154 (2019)
Sovacool, B.K., Furszyfer, D.D., Rio, D.: Smart home technologies in Europe: a critical review of concepts, benefits, risks and policies. Renew. Sustain. Energy Rev. 120, 109663 (2020)
Maalsen, S., Sadowski, J.: The smart home on FIRE: amplifying and accelerating domestic surveillance. Surveillance Soc. 17(1/2), 118–124 (2019)
Stolojescu-Crisan, C., Crisan, C., Butunoi, B.-P.: An IoT-based smart home automation system. Sensors 21(11), 3784 (2021)
Vujović, V., Maksimović, M.: Raspberry Pi as a sensor web node for home automation. Comput. Electr. Eng. 44, 153–171 (2015)
Ren, H., et al.: Time-series anomaly detection service at Microsoft. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2019)
Braei, M., Wagner, S.: Anomaly detection in univariate time-series: a survey on the state-of-the-art. arXiv preprint arXiv:2004.00433 (2020)
Tang, Y., et al.: Integrating prediction and reconstruction for anomaly detection. Pattern Recogn. Lett. 129, 123–130 (2020)
Thudumu, S., Branch, P., Jin, J., Singh, J.J.: A comprehensive survey of anomaly detection techniques for high dimensional big data. J. Big Data 7(1), 1–30 (2020). https://doi.org/10.1186/s40537-020-00320-x
Dimara, A., et al.: Self-healing of semantically interoperable smart and prescriptive edge devices in IoT. Appl. Sci. 12(22), 11650 (2022). https://doi.org/10.3390/app122211650
Kingma, D.P., Ba, J.: Adam: a method for stochastic optimization. arXiv preprint arXiv:1412.6980 (2014)
Gensler, A., Henze, J., Sick, B., Raabe, N.: Deep Learning for solar power forecasting-an approach using AutoEncoder and LSTM neural networks. In: 2016 IEEE International Conference on Systems, Man, and Cybernetics (SMC), pp. 002858–002865. IEEE, October 2016
Wang, B., Shi, W., Miao, Z.: Confidence analysis of standard deviational ellipse and its extension into higher dimensional Euclidean space. PLoS ONE 10(3), e0118537 (2015)
Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural Comput. 9(8), 1735–1780 (1997)
Li, Z., Liu, F., Yang, W., Peng, S., Zhou, J.: A survey of convolutional neural networks: analysis, applications, and prospects. IEEE Trans. Neural Netw. Learn. Syst. (2021)
Liu, F.T., Ting, K.M., Zhou, Z.H.: Isolation forest. In: 2008 Eighth IEEE International Conference on Data Mining, pp. 413–422. IEEE, December 2008
Schölkopf, B., Platt, J.C., Shawe-Taylor, J., Smola, A.J., Williamson, R.C.: Estimating the support of a high-dimensional distribution. Neural Comput. 13(7), 1443–1471 (2001)
Breunig, M.M., Kriegel, H.P., Ng, R.T., Sander, J.: LOF: identifying density-based local outliers. In: Proceedings of the 2000 ACM SIGMOD International Conference on Management of Data, pp. 93–104 (2000)
Ester, M., Kriegel, H.P., Sander, J., Xu, X.: A density-based algorithm for discovering clusters in large spatial databases with noise. In: Proceedings of the 2nd International Conference on Knowledge Discovery and Data Mining (KDD-96), pp. 226–231 (1996)
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.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2023 IFIP International Federation for Information Processing
About this paper
Cite this paper
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
Download citation
DOI: https://doi.org/10.1007/978-3-031-34171-7_18
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-34170-0
Online ISBN: 978-3-031-34171-7
eBook Packages: Computer ScienceComputer Science (R0)
