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

Wu, Hongde ORCID: 0000-0002-2038-1002, O'Connor, Noel E. ORCID: 0000-0002-4033-9135, Bruton, Jennifer, Hall, Amy ORCID: 0000-0002-3461-2385 and Liu, Mingming ORCID: 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

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Abstract

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)
Refereed:Yes
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 Initiatives and Centres > INSIGHT Centre for Data Analytics
Publisher:MDPI
Official URL:https://dx.doi.org/10.3390/s22165945
Copyright Information:© 2022 The Authors. Open Access (CC-BY 4.0)
Funders:SFI/12/RC/2289_P2
ID Code:27523
Deposited On:10 Aug 2022 09:40 by Mingming Liu . Last Modified 10 Aug 2022 09:40

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