{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,26]],"date-time":"2026-02-26T15:11:29Z","timestamp":1772118689068,"version":"3.50.1"},"reference-count":15,"publisher":"Wiley","issue":"1","license":[{"start":{"date-parts":[[2025,12,28]],"date-time":"2025-12-28T00:00:00Z","timestamp":1766880000000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"},{"start":{"date-parts":[[2025,12,28]],"date-time":"2025-12-28T00:00:00Z","timestamp":1766880000000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/doi.wiley.com\/10.1002\/tdm_license_1.1"}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Internet Technology Letters"],"published-print":{"date-parts":[[2026,1]]},"abstract":"<jats:title>ABSTRACT<\/jats:title>\n                  <jats:p>To address overheating and maintenance challenges in wind turbine slip ring systems operating in harsh environments, this study develops an online temperature monitoring system integrating Industrial Internet of Things (IIoT) and edge computing. A deep learning\u2010driven distributed resource allocation algorithm (DDLRA) is deployed on edge devices to achieve real\u2010time slip ring temperature monitoring, intelligent fault prediction, and reduced latency. A multi\u2010layer edge intelligence architecture is constructed, optimizing offloading strategies via distributed neural networks to balance energy efficiency and resource utilization. Experimental simulations using over 1000 temperature datasets from key components demonstrate that the model maintains prediction errors within 0.3\u00b0C, effectively identifying anomalies and adapting to variable conditions. The system also enables real\u2010time line load scheduling and carbon brush temperature prediction. This study pioneers the application of online updatable AI models in slip ring monitoring, combining edge responsiveness and cloud collaboration, offering significant engineering value for improving wind turbine reliability.<\/jats:p>","DOI":"10.1002\/itl2.70115","type":"journal-article","created":{"date-parts":[[2025,12,29]],"date-time":"2025-12-29T05:47:11Z","timestamp":1766987231000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Research on Online Monitoring System of Generator Slip Ring Temperature Based on the Internet of Things and Edge Computing"],"prefix":"10.1002","volume":"9","author":[{"ORCID":"https:\/\/orcid.org\/0009-0000-2916-9238","authenticated-orcid":false,"given":"Tang","family":"Li","sequence":"first","affiliation":[{"name":"Industrial Perception and Intelligent Manufacturing Equipment Engineering Research Center of Jiangsu Province, Nanjing Vocational University of Industry Technology  Nanjing China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"311","published-online":{"date-parts":[[2025,12,28]]},"reference":[{"key":"e_1_2_7_2_1","doi-asserted-by":"publisher","DOI":"10.1109\/JIOT.2020.3023126"},{"key":"e_1_2_7_3_1","doi-asserted-by":"publisher","DOI":"10.3390\/en15176140"},{"key":"e_1_2_7_4_1","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2020.2980060"},{"key":"e_1_2_7_5_1","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2021.3052469"},{"key":"e_1_2_7_6_1","doi-asserted-by":"publisher","DOI":"10.1109\/JIOT.2021.3067951"},{"key":"e_1_2_7_7_1","doi-asserted-by":"publisher","DOI":"10.1109\/COMST.2020.2970550"},{"key":"e_1_2_7_8_1","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2022.3166799"},{"key":"e_1_2_7_9_1","first-page":"1","article-title":"Power Flow Adjustment for Smart Microgrid Based on Edge Computing and Multi\u2010Agent Deep Reinforcement Learning","volume":"10","author":"Pu T.","year":"2021","journal-title":"Journal of Cloud Computing"},{"key":"e_1_2_7_10_1","doi-asserted-by":"publisher","DOI":"10.1109\/TCSS.2021.3063538"},{"key":"e_1_2_7_11_1","doi-asserted-by":"publisher","DOI":"10.1111\/exsy.13072"},{"key":"e_1_2_7_12_1","doi-asserted-by":"crossref","unstructured":"S.Tuli G.Casale andN. R.Jennings \u201cPregan: Preemptive Migration Prediction Network for Proactive Fault\u2010Tolerant Edge Computing \u201d inIEEE INFOCOM 2022\u2010IEEE Conference on Computer Communications. (IEEE 2022) 670\u2013679.","DOI":"10.1109\/INFOCOM48880.2022.9796778"},{"key":"e_1_2_7_13_1","doi-asserted-by":"publisher","DOI":"10.1155\/2020\/8832341"},{"key":"e_1_2_7_14_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.comnet.2021.108671"},{"key":"e_1_2_7_15_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.epsr.2022.108806"},{"key":"e_1_2_7_16_1","doi-asserted-by":"publisher","DOI":"10.1109\/TII.2022.3170149"}],"container-title":["Internet Technology Letters"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/pdf\/10.1002\/itl2.70115","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/full-xml\/10.1002\/itl2.70115","content-type":"application\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/pdf\/10.1002\/itl2.70115","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,1,23]],"date-time":"2026-01-23T03:41:58Z","timestamp":1769139718000},"score":1,"resource":{"primary":{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/10.1002\/itl2.70115"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,12,28]]},"references-count":15,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2026,1]]}},"alternative-id":["10.1002\/itl2.70115"],"URL":"https:\/\/doi.org\/10.1002\/itl2.70115","archive":["Portico"],"relation":{"has-review":[{"id-type":"doi","id":"10.1002\/ITL2.70115\/v1\/decision1","asserted-by":"object"},{"id-type":"doi","id":"10.1002\/ITL2.70115\/v2\/review2","asserted-by":"object"},{"id-type":"doi","id":"10.1002\/ITL2.70115\/v2\/decision1","asserted-by":"object"},{"id-type":"doi","id":"10.1002\/ITL2.70115\/v2\/response1","asserted-by":"object"},{"id-type":"doi","id":"10.1002\/ITL2.70115\/v1\/review1","asserted-by":"object"},{"id-type":"doi","id":"10.1002\/ITL2.70115\/v1\/review2","asserted-by":"object"},{"id-type":"doi","id":"10.1002\/ITL2.70115\/v2\/review1","asserted-by":"object"}]},"ISSN":["2476-1508","2476-1508"],"issn-type":[{"value":"2476-1508","type":"print"},{"value":"2476-1508","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,12,28]]},"assertion":[{"value":"2025-04-29","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2025-07-31","order":2,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2025-12-28","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}],"article-number":"e70115"}}