Fault Detection on the Edge and Adaptive Communication for State of Alert in Industrial Internet of Things
Industrial production and manufacturing systems require automation, reliability, as well as low-latency intelligent control. Industrial Internet of Things (IIoT) is an emerging paradigm that enables precise, low latency, intelligent computing, supported by cutting-edge technology such as edge comput...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-03-01
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| Series: | Sensors |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1424-8220/23/7/3544 |
| _version_ | 1797607041796145152 |
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| author | Yuri Santo Roger Immich Bruno L. Dalmazo André Riker |
| author_facet | Yuri Santo Roger Immich Bruno L. Dalmazo André Riker |
| author_sort | Yuri Santo |
| collection | DOAJ |
| description | Industrial production and manufacturing systems require automation, reliability, as well as low-latency intelligent control. Industrial Internet of Things (IIoT) is an emerging paradigm that enables precise, low latency, intelligent computing, supported by cutting-edge technology such as edge computing and machine learning. IIoT provides some of the essential building blocks to drive manufacturing systems to the next level of productivity, efficiency, and safety. Hardware failures and faults in IIoT are critical challenges to be faced. These anomalies can cause accidents and financial loss, affect productivity, and mobilize staff by producing false alarms. In this context, this article proposes a framework called Detection and Alert State for Industrial Internet of Things Faults (DASIF). The DASIF framework applies edge computing to execute highly precise and low latency machine learning models to detect industrial IoT faults and autonomously enforce an adaptive communication policy, triggering a state of alert in case of fault detection. The state of alert is a pre-stage countermeasure where the network increases communication reliability by using data replication combined with multiple-path communication. When the system is under alert, it can process a fine-grained inspection of the data for efficient decison-making. DASIF performance was obtained considering a simulation of the IIoT network and a real petrochemical dataset. |
| first_indexed | 2024-03-11T05:24:43Z |
| format | Article |
| id | doaj.art-589b826b43b044c683eadd5e1b269eb1 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-11T05:24:43Z |
| publishDate | 2023-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-589b826b43b044c683eadd5e1b269eb12023-11-17T17:34:05ZengMDPI AGSensors1424-82202023-03-01237354410.3390/s23073544Fault Detection on the Edge and Adaptive Communication for State of Alert in Industrial Internet of ThingsYuri Santo0Roger Immich1Bruno L. Dalmazo2André Riker3Institute of Exact and Natural Sciences (ICEN), Federal University of Pará, Belém 66075-110, BrazilMetropole Digital Institute (IMD), Federal University of Rio Grande do Norte (UFRN), Natal 59078-970, BrazilComputer Science Center (C3), Federal University of Rio Grande, Rio Grande 96203-900, BrazilInstitute of Exact and Natural Sciences (ICEN), Federal University of Pará, Belém 66075-110, BrazilIndustrial production and manufacturing systems require automation, reliability, as well as low-latency intelligent control. Industrial Internet of Things (IIoT) is an emerging paradigm that enables precise, low latency, intelligent computing, supported by cutting-edge technology such as edge computing and machine learning. IIoT provides some of the essential building blocks to drive manufacturing systems to the next level of productivity, efficiency, and safety. Hardware failures and faults in IIoT are critical challenges to be faced. These anomalies can cause accidents and financial loss, affect productivity, and mobilize staff by producing false alarms. In this context, this article proposes a framework called Detection and Alert State for Industrial Internet of Things Faults (DASIF). The DASIF framework applies edge computing to execute highly precise and low latency machine learning models to detect industrial IoT faults and autonomously enforce an adaptive communication policy, triggering a state of alert in case of fault detection. The state of alert is a pre-stage countermeasure where the network increases communication reliability by using data replication combined with multiple-path communication. When the system is under alert, it can process a fine-grained inspection of the data for efficient decison-making. DASIF performance was obtained considering a simulation of the IIoT network and a real petrochemical dataset.https://www.mdpi.com/1424-8220/23/7/3544Industrial Internet of Things (IIoT)machine learningedge computing |
| spellingShingle | Yuri Santo Roger Immich Bruno L. Dalmazo André Riker Fault Detection on the Edge and Adaptive Communication for State of Alert in Industrial Internet of Things Sensors Industrial Internet of Things (IIoT) machine learning edge computing |
| title | Fault Detection on the Edge and Adaptive Communication for State of Alert in Industrial Internet of Things |
| title_full | Fault Detection on the Edge and Adaptive Communication for State of Alert in Industrial Internet of Things |
| title_fullStr | Fault Detection on the Edge and Adaptive Communication for State of Alert in Industrial Internet of Things |
| title_full_unstemmed | Fault Detection on the Edge and Adaptive Communication for State of Alert in Industrial Internet of Things |
| title_short | Fault Detection on the Edge and Adaptive Communication for State of Alert in Industrial Internet of Things |
| title_sort | fault detection on the edge and adaptive communication for state of alert in industrial internet of things |
| topic | Industrial Internet of Things (IIoT) machine learning edge computing |
| url | https://www.mdpi.com/1424-8220/23/7/3544 |
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