[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-122987-en":3,"doc-seo-122987-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},122987,2336464648322,"Aria","https://ap-avatar.wpscdn.com/avatar/2200025388227c56fec?_k=1778556882303663488",6,"Technology","Driving Intelligent IoT Monitoring and Control through Cloud Computing and Machine Learning - Research","This article explores how to achieve intelligent IoT monitoring and control by combining cloud computing with machine learning. Sensor networks generate large, diverse data that can be uploaded to the cloud for statistical analysis, prediction, and data-driven decision support. When internet connectivity quality is insufficient for critical tasks, edge computing is introduced to shift processing and services toward the network edge, enabling near-end analysis with lower latency, higher efficiency, and better security. The work further presents IoT monitoring/control technology, edge-based application design, and ML roles in analysis and fault detection, validated through cases and experimental studies across industry, agriculture, and medical domains.","ACM-1  \nDriving Intelligent IoT Monitoring and Control through Cloud Computing and Machine Learning  \nHanzhe Li 1*  \nComputer Engineering, New York University, NY USA  \n* Corresponding [author:Nyhanzheli@gmail.com](author:Nyhanzheli@gmail.com)  \nXiangxiang Wang  \n1  \nComputer Science,University of Texas at Arlington,Arlington, TX, USA[wx18714999@gmail.com](wx18714999@gmail.com)  \nYuan Feng  \n2  \nInterdisciplinary Data Science ,Duke University ,North Carolina USA [yuan.feng.dsduke@gmail.com](yuan.feng.dsduke@gmail.com)  \nYaqian Qi 3  \nQuantitative Methods and Modeling,Baruch Collegue, CUNY ,55 Lexington Ave, NY, USA [alicia.qi.yaqian@gmail.com](alicia.qi.yaqian@gmail.com)  \nJingxiao Tian  \n4  \nElectrical and Computer Engineering,San Diego State University,SD, USA [jtian1125@sdsu.edu](jtian1125@sdsu.edu)  \nAbstract  \nThis article explores how to drive intelligent iot monitoring and control through cloud computing and machine learning. As iot and the cloud continue to generate large and diverse amounts of data as sensor devices in the network, the collected data is sent to the cloud for statistical analysis, prediction, and data analysis to achieve business objectives. However, because the cloud computing model is limited by distance, it can be problematic in environments where the quality of the Internet connection is not ideal for critical operations. Therefore, edge computing, as a distributed computing architecture, moves the location of processing applications, data and services from the central node of the network to the logical edge node of the network to reduce the dependence on cloud processing and analysis of data, and achieve near-end data processing and analysis. The combination of iot and edge computing can reduce latency, improve efficiency, and enhance security, thereby driving the development of  \nACM-2  \nintelligent systems. The paper also introduces the development of iot monitoring and control technology, the application of edge computing in iot monitoring and control, and the role of machine learning in data analysis and fault detection. Finally, the application and effect of intelligent Internet of Things monitoring and control system in industry, agriculture, medical and other fields are demonstrated through practical cases and experimental studies.  \nkey words :  \nInternet of Things;Cloud Computing;Edge Computing；Real-time Information and Analytics  \n1 INTRODUCTION  \nThe origins of iot monitoring and control technology can be traced back to the 1980s, when remote monitoring and control systems began to appear in the field of industrial automation. With the development of computer and communication technology, people began to try to connect sensors and actuators with the network to achieve remote monitoring and control. In 1999, an MIT study first proposed the concept of the \"Internet of Things,\" meaning that objects can be connected and communicated with each other through a network, enabling information sharing and intelligent control. Since then, Internet of Things monitoring and control technology has gradually become a research hotspot in industry, agriculture, health and other fields. Key technological breakthroughs include the development of sensor technology, advances in wireless communication technology, and improvements in data processing and analysis algorithms. With the continuous evolution of Internet of Things technology, more and more application scenarios have been realized, such as smart homes, smart cities, smart factories, etc., bringing convenience and efficiency improvement to people's life and work.  \nWith the gradual maturity of Internet of Things technology, Internet of Things monitoring and control have been widely used in various fields. In the industrial sector, iot monitoring and control technology can enable intelligent manufacturing, including equipment condition monitoring, production process optimization and predictive maintenance. In agriculture, iot monitoring and control te","cbCaihBR6Lsi5eaA","https://ap.wps.com/l/cbCaihBR6Lsi5eaA","pdf",1185556,1,15,"English","en",105,"# Introduction\n# Related Work\n## Application of edge computing in IoT monitoring and control","[{\"question\":\"How does the paper approach intelligent IoT monitoring and control?\",\"answer\":\"It combines cloud computing and machine learning to process sensor data for analysis and prediction, while using edge computing to support near-end processing when cloud connectivity is unreliable.\"},{\"question\":\"Why is edge computing emphasized compared with relying only on cloud processing?\",\"answer\":\"Edge computing reduces dependence on centralized cloud analysis by moving processing closer to the data source, which lowers latency and improves efficiency and security.\"},{\"question\":\"What role does machine learning play in the proposed system?\",\"answer\":\"Machine learning is used for data analysis and for tasks such as prediction and fault detection to support intelligent monitoring and control.\"}]","Driving Intelligent IoT Monitoring and Control through Cloud Computing and Machine Learning - 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