RIS-aided smart manufacturing: information transmission and machine health monitoring
Article
Hoang, T., Son, D., Barn, B., Trestian, R. and Nguyen, H. 2022. RIS-aided smart manufacturing: information transmission and machine health monitoring. IEEE Internet of Things Journal. 9 (22), pp. 22930-22943. https://doi.org/10.1109/JIOT.2022.3187189
Type | Article |
---|---|
Title | RIS-aided smart manufacturing: information transmission and machine health monitoring |
Authors | Hoang, T., Son, D., Barn, B., Trestian, R. and Nguyen, H. |
Abstract | This paper proposes a novel industrial Internet-of-Things framework to monitor the machine health conditions (MHCs) in a smart factory. The framework utilises reconfigurable intelligent surface (RIS) to address propagation blockages while employing a novel power mapping scheme and an autoencoder to facilitate the transmission and classification of the MHCs. Analytical and numerical analyses are then performed to study the ergodic capacity (primary information) and the MHC accuracy (secondary information) in terms of the RIS size (K) and the transmit power (P). We observe that the accuracy of detecting MHCs does not change significantly with K and P , implying that the MHC alerts can be efficiently conveyed in parallel with the primary information. By contrast, a careful choice of different power mapping levels is necessary in order to achieve the two main goals: i) reasonably high data rate for primary transmission and ii) high accuracy for secondary MHC information. |
Keywords | 5G/6G; autoencoder (AE); digital twin; industrial Internet of Thing (IoT); Industry 4.0; machine health; reconfigurable intelligent surface (RIS) |
Sustainable Development Goals | 9 Industry, innovation and infrastructure |
Publisher | IEEE |
Journal | IEEE Internet of Things Journal |
ISSN | |
Electronic | 2327-4662 |
Publication dates | |
Online | 29 Jun 2022 |
15 Nov 2022 | |
Publication process dates | |
Deposited | 05 Jul 2022 |
Accepted | 16 Jun 2022 |
Accepted author manuscript | |
Copyright Statement | © 2022 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. |
Digital Object Identifier (DOI) | https://doi.org/10.1109/JIOT.2022.3187189 |
Web of Science identifier | WOS:000879049400076 |
Language | English |
https://repository.mdx.ac.uk/item/89x5w
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