{"title":"基于微型机器学习的边缘云合作校园网自媒体舆情实时异常检测","authors":"Shiqi Li","doi":"10.1002/itl2.70038","DOIUrl":null,"url":null,"abstract":"<div>\n \n <p>Real-time anomaly detection in self-media public opinion requires lightweight solutions to address the latency and multimodal complexity challenges of campus network ecosystems. This article proposes a tiny machine learning framework for edge-cloud-cooperation campus networks, enabling efficient detection of opinion anomalies through distributed computation. The architecture combines edge-native micro-model compression with cloud-assisted federated verification, achieving three key innovations: (1) On-device micro-graph neural networks (GNNs) deployed at edge nodes for low-latency pattern recognition in terahertz multimedia streams; (2) a dual-phase anomaly engine leveraging contrastive semantic alignment and adaptive influence analysis to capture cross-modal inconsistencies; (3) dynamic knowledge distillation that reduces model footprints to 8 MB while preserving 91% precision and 87% recall on a 120,000 post dataset from 15 universities. Experimental results demonstrate 120 ms average inference latency with 68% lower computation overhead than centralized baselines, accelerating emergency response by 3.25× through edge-cloud task partitioning. The framework maintains 74% energy efficiency in continuous operation, proving the viability of tiny machine learning paradigms for intelligent campus governance without relying on next-generation communication standards.</p>\n </div>","PeriodicalId":100725,"journal":{"name":"Internet Technology Letters","volume":"8 4","pages":""},"PeriodicalIF":0.5000,"publicationDate":"2025-05-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"A Tiny Machine Learning for Real-Time Anomaly Detection of Self-Media Public Opinion in Edge-Cloud-Cooperation Campus Networks\",\"authors\":\"Shiqi Li\",\"doi\":\"10.1002/itl2.70038\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div>\\n \\n <p>Real-time anomaly detection in self-media public opinion requires lightweight solutions to address the latency and multimodal complexity challenges of campus network ecosystems. This article proposes a tiny machine learning framework for edge-cloud-cooperation campus networks, enabling efficient detection of opinion anomalies through distributed computation. The architecture combines edge-native micro-model compression with cloud-assisted federated verification, achieving three key innovations: (1) On-device micro-graph neural networks (GNNs) deployed at edge nodes for low-latency pattern recognition in terahertz multimedia streams; (2) a dual-phase anomaly engine leveraging contrastive semantic alignment and adaptive influence analysis to capture cross-modal inconsistencies; (3) dynamic knowledge distillation that reduces model footprints to 8 MB while preserving 91% precision and 87% recall on a 120,000 post dataset from 15 universities. Experimental results demonstrate 120 ms average inference latency with 68% lower computation overhead than centralized baselines, accelerating emergency response by 3.25× through edge-cloud task partitioning. The framework maintains 74% energy efficiency in continuous operation, proving the viability of tiny machine learning paradigms for intelligent campus governance without relying on next-generation communication standards.</p>\\n </div>\",\"PeriodicalId\":100725,\"journal\":{\"name\":\"Internet Technology Letters\",\"volume\":\"8 4\",\"pages\":\"\"},\"PeriodicalIF\":0.5000,\"publicationDate\":\"2025-05-28\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Internet Technology Letters\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://onlinelibrary.wiley.com/doi/10.1002/itl2.70038\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q4\",\"JCRName\":\"TELECOMMUNICATIONS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Internet Technology Letters","FirstCategoryId":"1085","ListUrlMain":"https://onlinelibrary.wiley.com/doi/10.1002/itl2.70038","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"TELECOMMUNICATIONS","Score":null,"Total":0}
A Tiny Machine Learning for Real-Time Anomaly Detection of Self-Media Public Opinion in Edge-Cloud-Cooperation Campus Networks
Real-time anomaly detection in self-media public opinion requires lightweight solutions to address the latency and multimodal complexity challenges of campus network ecosystems. This article proposes a tiny machine learning framework for edge-cloud-cooperation campus networks, enabling efficient detection of opinion anomalies through distributed computation. The architecture combines edge-native micro-model compression with cloud-assisted federated verification, achieving three key innovations: (1) On-device micro-graph neural networks (GNNs) deployed at edge nodes for low-latency pattern recognition in terahertz multimedia streams; (2) a dual-phase anomaly engine leveraging contrastive semantic alignment and adaptive influence analysis to capture cross-modal inconsistencies; (3) dynamic knowledge distillation that reduces model footprints to 8 MB while preserving 91% precision and 87% recall on a 120,000 post dataset from 15 universities. Experimental results demonstrate 120 ms average inference latency with 68% lower computation overhead than centralized baselines, accelerating emergency response by 3.25× through edge-cloud task partitioning. The framework maintains 74% energy efficiency in continuous operation, proving the viability of tiny machine learning paradigms for intelligent campus governance without relying on next-generation communication standards.