5G Architectures Enabling Remaining Useful Life Estimation for Industrial IoT: A Holistic Study
In the ever-evolving landscape of industrial connectivity, significant strides have been made in the integration of 5th generation (5G) cellular technology with Industrial Internet of Things (IIoT) systems. At the same time, data-driven analytics has become an effective tool for leveraging informati...
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| Format: | Article |
| Language: | English |
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IEEE
2025-01-01
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| Series: | IEEE Open Journal of the Communications Society |
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| Online Access: | https://ieeexplore.ieee.org/document/10843389/ |
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| author | Nicolo Longhi Lorenzo Mario Amorosa Sara Cavallero Enrico Buracchini Roberto Verdone |
| author_facet | Nicolo Longhi Lorenzo Mario Amorosa Sara Cavallero Enrico Buracchini Roberto Verdone |
| author_sort | Nicolo Longhi |
| collection | DOAJ |
| description | In the ever-evolving landscape of industrial connectivity, significant strides have been made in the integration of 5th generation (5G) cellular technology with Industrial Internet of Things (IIoT) systems. At the same time, data-driven analytics has become an effective tool for leveraging information from interconnected industrial devices, enabling organizations to gain valuable insights and make informed decisions. However, among these advancements, the holistic perspective of end-to-end analysis related to their integration remains a critical aspect that has yet to be comprehensively addressed. To this end, we investigate 5G IIoT network architectures that support automated guided vehicles (AGVs) on a factory floor as an illustrative example. In particular, we leverage real sensor data collected by AGVs to estimate their remaining useful life (RUL) using a deep learning (DL)-based pipeline. We conduct an in-depth analysis to assess the compatibility of 5G New Radio infrastructures with the aforementioned case study, focusing on round trip time (RTT) requirements and emphasizing the inter-dependencies between communication network and data-driven application. |
| format | Article |
| id | doaj-art-868faa17cd0749fcbc11a0864d5800fe |
| institution | OA Journals |
| issn | 2644-125X |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Open Journal of the Communications Society |
| spelling | doaj-art-868faa17cd0749fcbc11a0864d5800fe2025-08-20T02:26:45ZengIEEEIEEE Open Journal of the Communications Society2644-125X2025-01-0161016102910.1109/OJCOMS.2025.3530094108433895G Architectures Enabling Remaining Useful Life Estimation for Industrial IoT: A Holistic StudyNicolo Longhi0https://orcid.org/0009-0001-5179-5013Lorenzo Mario Amorosa1https://orcid.org/0000-0002-0405-9611Sara Cavallero2https://orcid.org/0000-0003-4302-6297Enrico Buracchini3Roberto Verdone4https://orcid.org/0000-0001-6522-8573Department of Electrical, Electronic and Information Engineering, ”Guglielmo Marconi”, University of Bologna, Bologna, ItalyDepartment of Electrical, Electronic and Information Engineering, ”Guglielmo Marconi”, University of Bologna, Bologna, ItalyDepartment of Electrical, Electronic and Information Engineering, ”Guglielmo Marconi”, University of Bologna, Bologna, ItalyNetwork Innovation Department, Telecom Italia S.p.A, Rome, ItalyDepartment of Electrical, Electronic and Information Engineering, ”Guglielmo Marconi”, University of Bologna, Bologna, ItalyIn the ever-evolving landscape of industrial connectivity, significant strides have been made in the integration of 5th generation (5G) cellular technology with Industrial Internet of Things (IIoT) systems. At the same time, data-driven analytics has become an effective tool for leveraging information from interconnected industrial devices, enabling organizations to gain valuable insights and make informed decisions. However, among these advancements, the holistic perspective of end-to-end analysis related to their integration remains a critical aspect that has yet to be comprehensively addressed. To this end, we investigate 5G IIoT network architectures that support automated guided vehicles (AGVs) on a factory floor as an illustrative example. In particular, we leverage real sensor data collected by AGVs to estimate their remaining useful life (RUL) using a deep learning (DL)-based pipeline. We conduct an in-depth analysis to assess the compatibility of 5G New Radio infrastructures with the aforementioned case study, focusing on round trip time (RTT) requirements and emphasizing the inter-dependencies between communication network and data-driven application.https://ieeexplore.ieee.org/document/10843389/5G5G new radioautomated guided vehiclesdeep learningindustrial Internet of Thingsremaining useful life |
| spellingShingle | Nicolo Longhi Lorenzo Mario Amorosa Sara Cavallero Enrico Buracchini Roberto Verdone 5G Architectures Enabling Remaining Useful Life Estimation for Industrial IoT: A Holistic Study IEEE Open Journal of the Communications Society 5G 5G new radio automated guided vehicles deep learning industrial Internet of Things remaining useful life |
| title | 5G Architectures Enabling Remaining Useful Life Estimation for Industrial IoT: A Holistic Study |
| title_full | 5G Architectures Enabling Remaining Useful Life Estimation for Industrial IoT: A Holistic Study |
| title_fullStr | 5G Architectures Enabling Remaining Useful Life Estimation for Industrial IoT: A Holistic Study |
| title_full_unstemmed | 5G Architectures Enabling Remaining Useful Life Estimation for Industrial IoT: A Holistic Study |
| title_short | 5G Architectures Enabling Remaining Useful Life Estimation for Industrial IoT: A Holistic Study |
| title_sort | 5g architectures enabling remaining useful life estimation for industrial iot a holistic study |
| topic | 5G 5G new radio automated guided vehicles deep learning industrial Internet of Things remaining useful life |
| url | https://ieeexplore.ieee.org/document/10843389/ |
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