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An ADMM-based optimal transmission frequency management system for IoT edge intelligence

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 and Liu, Mingming orcid logoORCID: 0000-0002-8988-2104 (2021) An ADMM-based optimal transmission frequency management system for IoT edge intelligence. In: 7th IEEE World Forum on Internet of Things, 20-24 June 2021, New Orleans, USA and Online.

In this paper, we investigate a key problem of Internet of Things (IoT) applications in practice. Our research objective is to optimize the transmission frequencies for a group of IoT edge devices under practical constraints. Our key assumption is that different IoT devices may have different priority levels when transmitting data in a resource-constrained environment and that those priority levels may only be locally defined and accessible by edge devices for privacy concerns. To address this problem, we leverage the well-known Alternating Direction Method of Multipliers (ADMM) optimization method and demonstrate its applicability for effectively managing various IoT data streams in a decentralized framework. Our experimental results show that the transmission frequency of each edge device can converge to optimality with little delay using ADMM, and the proposed system can be adjusted dynamically when a new device connects to the system. In addition, we also introduce an anomaly detection mechanism to the system when a device's transmission frequency may be compromised by external manipulation, showing that the proposed system is robust and secure for various IoT applications.
Item Type:Conference or Workshop Item (Paper)
Event Type:Conference
Uncontrolled Keywords:Internet of Things; Decentralized Algorithms; Edge Intelligence
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
Published in: 2021 IEEE 7th World Forum on Internet of Things (WF-IoT). . IEEE.
Official URL:https://dx.doi.org/10.1109/WF-IoT51360.2021.959535...
Copyright Information:© 2021 The Authors
Use License:This item is licensed under a Creative Commons Attribution-NonCommercial-Share Alike 3.0 License. View License
Funders:Science Foundation Ireland SFI/12/RC/2289 P2, Postgraduate Research Scholarship from the Faculty of Engineering and Computing at Dublin City University
ID Code:25764
Deposited On:21 Jun 2021 17:10 by Mingming Liu . Last Modified 26 Sep 2023 09:09

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