{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,24]],"date-time":"2026-08-24T23:36:53Z","timestamp":1787614613194,"version":"build-2736575974"},"reference-count":21,"publisher":"Wiley","issue":"3","license":[{"start":{"date-parts":[[2026,3,27]],"date-time":"2026-03-27T00:00:00Z","timestamp":1774569600000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"},{"start":{"date-parts":[[2026,3,27]],"date-time":"2026-03-27T00:00:00Z","timestamp":1774569600000},"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":[[2026,5]]},"abstract":"<jats:title>ABSTRACT<\/jats:title>\n                  <jats:p>The rapid expansion of the Industrial Internet of Things (IIoT) enables fine\u2010grained sensing and distributed actuation for demand\u2010side flexibility, but also introduces large\u2010scale heterogeneous load networks that must coordinate in real time under time\u2010varying connectivity and coupled constraints. Under the Industry 5.0 paradigm, such coordination requires not only efficiency but also resilience and transparency under unreliable communications. To address these challenges, this paper proposes a Graph Attention Diffusion\u2013based Flexible Response Optimization (GAD\u2010FRO) algorithm, which integrates attention\u2010guided diffusion operators into a distributed primal\u2013dual framework. GAD\u2010FRO achieves topology\u2010aware and communication\u2010robust coordination without centralized control by adaptively prioritizing reliable neighbors and smoothing information propagation over dynamic graphs. Experiments on four benchmark datasets (Pecan Street, Building Data Genome 2, UK\u2010DALE, and ACN\u2010Data) demonstrate that GAD\u2010FRO achieves faster convergence, lower objective cost, and stronger robustness to packet loss than representative distributed baselines, while approaching centralized oracle performance with low runtime and communication overhead.<\/jats:p>","DOI":"10.1002\/itl2.70253","type":"journal-article","created":{"date-parts":[[2026,3,27]],"date-time":"2026-03-27T07:51:07Z","timestamp":1774597867000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["A Graph Attention Diffusion Approach for Multi\u2010Node Flexible Response Optimization in\n                    <scp>IIoT<\/scp>\n                    Load Networks"],"prefix":"10.1002","volume":"9","author":[{"ORCID":"https:\/\/orcid.org\/0009-0006-5503-3792","authenticated-orcid":false,"given":"Weinan","family":"Zheng","sequence":"first","affiliation":[{"name":"Power Dispatching and Control Center Generation Management Department Shenzhen Power Supply Bureau Co., Ltd. of China Southern Power Grid  Shenzhen Guangdong China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yang","family":"Zhang","sequence":"additional","affiliation":[{"name":"Power Dispatching and Control Center Generation Management Department Shenzhen Power Supply Bureau Co., Ltd. of China Southern Power Grid  Shenzhen Guangdong China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhuang","family":"Dou","sequence":"additional","affiliation":[{"name":"Power Dispatching and Control Center Generation Management Department Shenzhen Power Supply Bureau Co., Ltd. of China Southern Power Grid  Shenzhen Guangdong China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Han","family":"Zheng","sequence":"additional","affiliation":[{"name":"Power Dispatching and Control Center Generation Management Department Shenzhen Power Supply Bureau Co., Ltd. of China Southern Power Grid  Shenzhen Guangdong China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2026,3,27]]},"reference":[{"key":"e_1_2_9_2_1","doi-asserted-by":"publisher","DOI":"10.1109\/TITS.2022.3226198"},{"key":"e_1_2_9_3_1","doi-asserted-by":"publisher","DOI":"10.1109\/TSP.2023.3282071"},{"key":"e_1_2_9_4_1","doi-asserted-by":"publisher","DOI":"10.1109\/JIOT.2024.3416054"},{"key":"e_1_2_9_5_1","doi-asserted-by":"publisher","DOI":"10.1109\/JIOT.2021.3092360"},{"key":"e_1_2_9_6_1","doi-asserted-by":"publisher","DOI":"10.1109\/JIOT.2025.3587658"},{"key":"e_1_2_9_7_1","doi-asserted-by":"publisher","DOI":"10.1109\/TCCN.2025.3577323"},{"key":"e_1_2_9_8_1","doi-asserted-by":"publisher","DOI":"10.1109\/TCSVT.2024.3407131"},{"key":"e_1_2_9_9_1","doi-asserted-by":"publisher","DOI":"10.1038\/s41598-025-24963-z"},{"key":"e_1_2_9_10_1","doi-asserted-by":"publisher","DOI":"10.1109\/TCCN.2025.3531486"},{"key":"e_1_2_9_11_1","doi-asserted-by":"publisher","DOI":"10.1109\/JIOT.2025.3590507"},{"key":"e_1_2_9_12_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-981-95-1581-3_12"},{"key":"e_1_2_9_13_1","doi-asserted-by":"publisher","DOI":"10.1109\/TITS.2025.3571210"},{"key":"e_1_2_9_14_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2021.03.140"},{"key":"e_1_2_9_15_1","doi-asserted-by":"publisher","DOI":"10.1109\/JIOT.2023.3290401"},{"key":"e_1_2_9_16_1","doi-asserted-by":"publisher","DOI":"10.1038\/s41598-025-94624-8"},{"key":"e_1_2_9_17_1","doi-asserted-by":"publisher","DOI":"10.1109\/TII.2023.3308784"},{"key":"e_1_2_9_18_1","first-page":"9585","volume-title":"Advances in Neural Information Processing Systems","author":"Liu Y.","year":"2024"},{"key":"e_1_2_9_19_1","doi-asserted-by":"crossref","unstructured":"J.Liao J.Wen J.Kang et al. \u201cOptimizing Information Propagation for Blockchain\u2010Empowered Mobile AIGC: A Graph Attention Network Approach \u201d2024 685\u2013690.","DOI":"10.1109\/IWCMC61514.2024.10592330"},{"key":"e_1_2_9_20_1","doi-asserted-by":"publisher","DOI":"10.1109\/JSAC.2025.3640136"},{"key":"e_1_2_9_21_1","doi-asserted-by":"crossref","unstructured":"M.Yenugula B.Konda V. K.Kasula A. R.Yadulla C.Tumma andS. B.Rakki \u201cA Graph Neural Diffusion Network for Sophisticated Persistent Threat Hunting in IoT Environments \u201d2025 1\u20136.","DOI":"10.1109\/ICCA65395.2025.11011108"},{"key":"e_1_2_9_22_1","doi-asserted-by":"publisher","DOI":"10.1109\/JIOT.2024.3446048"}],"container-title":["Internet Technology Letters"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/pdf\/10.1002\/itl2.70253","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/full-xml\/10.1002\/itl2.70253","content-type":"application\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/pdf\/10.1002\/itl2.70253","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,5,16]],"date-time":"2026-05-16T04:12:02Z","timestamp":1778904722000},"score":1,"resource":{"primary":{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/10.1002\/itl2.70253"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,3,27]]},"references-count":21,"journal-issue":{"issue":"3","published-print":{"date-parts":[[2026,5]]}},"alternative-id":["10.1002\/itl2.70253"],"URL":"https:\/\/doi.org\/10.1002\/itl2.70253","archive":["Portico"],"relation":{},"ISSN":["2476-1508","2476-1508"],"issn-type":[{"value":"2476-1508","type":"print"},{"value":"2476-1508","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,3,27]]},"assertion":[{"value":"2026-01-07","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2026-03-02","order":2,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2026-03-27","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}],"article-number":"e70253"}}