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Article

Real-Time Anomaly Detection for an ADMM-Based Optimal Transmission Frequency Management System for IoT Devices

1
School of Electronic Engineering, Dublin City University, Dublin 9, Ireland
2
Insight SFI Research Centre for Data Analytics, Dublin City University, Dublin 9, Ireland
*
Author to whom correspondence should be addressed.
Academic Editors: Kyandoghere Kyamakya, Fadi Al-Machot, Ahmad Haj Mosa, Jean Chamberlain Chedjou, Zhong Li and Antoine Bagula
Sensors 2022, 22(16), 5945; https://doi.org/10.3390/s22165945
Received: 14 July 2022 / Revised: 5 August 2022 / Accepted: 7 August 2022 / Published: 9 August 2022
In this paper, we investigate different scenarios of anomaly detection on decentralised Internet of Things (IoT) applications. Specifically, an anomaly detector is devised to detect different types of anomalies for an IoT data management system, based on the decentralised alternating direction method of multipliers (ADMM), which was proposed in our previous work. The anomaly detector only requires limited information from the IoT system, and can be operated using both a mathematical-rule-based approach and the deep learning approach proposed in the paper. Our experimental results show that detection based on mathematical approach is simple to implement, but it also comes with lower detection accuracy (78.88%). In contrast, the deep-learning-enabled approach can easily achieve a higher detection accuracy (96.28%) in the real world working environment. View Full-Text
Keywords: anomaly detection; Internet of Things; decentralised algorithms; edge intelligence anomaly detection; Internet of Things; decentralised algorithms; edge intelligence
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MDPI and ACS Style

Wu, H.; O’Connor, N.E.; Bruton, J.; Hall, A.; Liu, M. Real-Time Anomaly Detection for an ADMM-Based Optimal Transmission Frequency Management System for IoT Devices. Sensors 2022, 22, 5945. https://doi.org/10.3390/s22165945

AMA Style

Wu H, O’Connor NE, Bruton J, Hall A, Liu M. Real-Time Anomaly Detection for an ADMM-Based Optimal Transmission Frequency Management System for IoT Devices. Sensors. 2022; 22(16):5945. https://doi.org/10.3390/s22165945

Chicago/Turabian Style

Wu, Hongde, Noel E. O’Connor, Jennifer Bruton, Amy Hall, and Mingming Liu. 2022. "Real-Time Anomaly Detection for an ADMM-Based Optimal Transmission Frequency Management System for IoT Devices" Sensors 22, no. 16: 5945. https://doi.org/10.3390/s22165945

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