{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,24]],"date-time":"2026-06-24T15:26:14Z","timestamp":1782314774671,"version":"3.54.5"},"reference-count":20,"publisher":"MDPI AG","issue":"7","license":[{"start":{"date-parts":[[2023,3,28]],"date-time":"2023-03-28T00:00:00Z","timestamp":1679961600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"PROPESP\/UFPA (PAPQ)"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>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.<\/jats:p>","DOI":"10.3390\/s23073544","type":"journal-article","created":{"date-parts":[[2023,3,29]],"date-time":"2023-03-29T01:33:00Z","timestamp":1680053580000},"page":"3544","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":22,"title":["Fault Detection on the Edge and Adaptive Communication for State of Alert in Industrial Internet of Things"],"prefix":"10.3390","volume":"23","author":[{"given":"Yuri","family":"Santo","sequence":"first","affiliation":[{"name":"Institute of Exact and Natural Sciences (ICEN), Federal University of Par\u00e1, Bel\u00e9m 66075-110, Brazil"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2483-6382","authenticated-orcid":false,"given":"Roger","family":"Immich","sequence":"additional","affiliation":[{"name":"Metropole Digital Institute (IMD), Federal University of Rio Grande do Norte (UFRN), Natal 59078-970, Brazil"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6996-7602","authenticated-orcid":false,"given":"Bruno L.","family":"Dalmazo","sequence":"additional","affiliation":[{"name":"Computer Science Center (C3), Federal University of Rio Grande, Rio Grande 96203-900, Brazil"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Andr\u00e9","family":"Riker","sequence":"additional","affiliation":[{"name":"Institute of Exact and Natural Sciences (ICEN), Federal University of Par\u00e1, Bel\u00e9m 66075-110, Brazil"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2023,3,28]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"6144","DOI":"10.1109\/TII.2020.3044930","article-title":"EdgeKE: An on-demand deep learning IoT system for cognitive big data on industrial edge devices","volume":"17","author":"Fang","year":"2020","journal-title":"IEEE Trans. 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