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Real-time anomaly detection for an ADMM-based optimal transmission frequency management system for IoT devices

Wu, Hongde orcid logoORCID: 0000-0002-2038-1002, O'Connor, Noel E. orcid logoORCID: 0000-0002-4033-9135, Bruton, Jennifer orcid logoORCID: 0000-0001-5788-7579, Hall, Amy orcid logoORCID: 0000-0002-3461-2385 and Liu, Mingming orcid logoORCID: 0000-0002-8988-2104 (2022) Real-time anomaly detection for an ADMM-based optimal transmission frequency management system for IoT devices. Sensors, 22 (16). ISSN 1424-8220

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.
Item Type:Article (Published)
Uncontrolled Keywords:anomaly detection; Internet of Things; decentralised algorithms; edge intelligence
Subjects:Computer Science > Algorithms
Computer Science > Artificial intelligence
Computer Science > Machine learning
Engineering > Systems engineering
DCU Faculties and Centres:DCU Faculties and Schools > Faculty of Engineering and Computing > School of Electronic Engineering
Research Institutes and Centres > INSIGHT Centre for Data Analytics
Official URL:https://dx.doi.org/10.3390/s22165945
Copyright Information:© 2022 The Authors. Open Access (CC-BY 4.0)
ID Code:27523
Deposited On:10 Aug 2022 09:40 by Mingming Liu . Last Modified 26 Sep 2023 08:31

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