{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,24]],"date-time":"2026-01-24T10:14:36Z","timestamp":1769249676118,"version":"3.49.0"},"reference-count":20,"publisher":"Wiley","issue":"1","license":[{"start":{"date-parts":[[2025,12,12]],"date-time":"2025-12-12T00:00:00Z","timestamp":1765497600000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"},{"start":{"date-parts":[[2025,12,12]],"date-time":"2025-12-12T00:00:00Z","timestamp":1765497600000},"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>Efficient detection of abnormal student classroom behavior is of great significance for improving teaching quality and maintaining classroom order. However, traditional methods based on centralized cloud computing face the challenge of poor real\u2010time performance. In addition, existing video\u2010based anomaly action detection networks are difficult to deploy effectively on resource\u2010efficient edge servers. Therefore, this article proposes an efficient variant spatiotemporal graph convolution (ST\u2010GCN) model based on distributed edge collaboration learning. First, we deploy multiple edge servers and cameras in the classroom to form a distributed collaborative perception network. Second, we integrate the multi\u2010scale graph convolution and the multi\u2010view fusion mechanism to enhance the robustness of feature expression. Finally, we construct a collaborative learning mechanism to improve the generalization ability of abnormal action recognition. The experimental results on our self\u2010built classroom abnormal behavior dataset show that our proposed method outperforms existing graph convolution networks in terms of detection accuracy.<\/jats:p>","DOI":"10.1002\/itl2.70206","type":"journal-article","created":{"date-parts":[[2025,12,12]],"date-time":"2025-12-12T17:49:18Z","timestamp":1765561758000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Real\u2010Time Student Abnormal Behavior Detection Based on Distributed Edge Collaboration and Multi\u2010View Fusion Under Classroom Scene"],"prefix":"10.1002","volume":"9","author":[{"ORCID":"https:\/\/orcid.org\/0009-0003-6423-7103","authenticated-orcid":false,"given":"Haipeng","family":"Wu","sequence":"first","affiliation":[{"name":"Jinzhong College of Information  Shanxi China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"311","published-online":{"date-parts":[[2025,12,12]]},"reference":[{"key":"e_1_2_9_2_1","doi-asserted-by":"publisher","DOI":"10.11648\/j.ijsedu.20150305.11"},{"key":"e_1_2_9_3_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICASSP40776.2020.9053457"},{"issue":"21","key":"e_1_2_9_4_1","first-page":"44318","article-title":"Deep Learning Advancements in Anomaly Detection: A Comprehensive Survey","volume":"12","author":"Huang H.","year":"2025","journal-title":"IEEE Internet of Things 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Activity Recognition: A Spatio\u2010Temporal Image Encoding of 3d Skeleton Data for Online Action Detection","author":"Mokhtari N.","year":"2022"},{"key":"e_1_2_9_18_1","first-page":"1110","volume-title":"Hierarchical Recurrent Neural Network for Skeleton Based Action Recognition","author":"Du Y.","year":"2015"},{"key":"e_1_2_9_19_1","first-page":"7444","volume-title":"Spatial Temporal Graph Convolutional Networks for Skeleton\u2010Based Action Recognition","author":"Yan S.","year":"2018"},{"key":"e_1_2_9_20_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.01230"},{"key":"e_1_2_9_21_1","first-page":"936","volume-title":"Iip\u2010Transformer: Intra\u2010Inter\u2010Part Transformer for Skeleton\u2010Based Action Recognition","author":"Wang Q.","year":"2023"}],"container-title":["Internet Technology 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