Guastella, DA and Pournaras, E (2021) Learn to Sense vs. Sense to Learn: A System Self-Integration Approach. In: 2021 IEEE International Conference on Autonomic Computing and Self-Organizing Systems Companion (ACSOS-C). 2021 IEEE International Conference on Autonomic Computing and Self-Organizing Systems Companion (ACSOS-C), 27 Sep - 01 Oct 2021, Washington, DC, USA. IEEE , pp. 178-179. ISBN 978-1-6654-4393-7
Abstract
The diffusion of Internet of Things (IoT) devices has opened up new opportunities for decentralized data analytics. In this context, data transmission can be affected by both network issues and distance between devices and receivers. These factors can affect the ability to aggregate and analyze data from multiple IoT devices, resulting in noisy, partial, or incorrect information. To this end, self-healing techniques pursue corrective actions when information acquired from sensors is not reliable. In this paper, we propose a new self-integration approach to improve the performance of decentralized self-healing techniques.
Metadata
Item Type: | Proceedings Paper |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2021 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. |
Keywords: | Self-Healing, Multi-Agent Systems, Cooperative Learning |
Dates: |
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Institution: | The University of Leeds |
Academic Units: | The University of Leeds > Faculty of Engineering & Physical Sciences (Leeds) > School of Computing (Leeds) |
Depositing User: | Symplectic Publications |
Date Deposited: | 15 Feb 2022 15:57 |
Last Modified: | 15 Feb 2022 15:57 |
Status: | Published |
Publisher: | IEEE |
Identification Number: | 10.1109/ACSOS-C52956.2021.00053 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:183583 |