[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-120953-en":3,"doc-seo-120953-105":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},120953,549758252649,"Ivy","https://ap-avatar.wpscdn.com/avatar/8000253669c5317157?_k=1778319167496531819",8,"Research & Report","Methodology for Predicting Failures in a Smart Home based on Machine Learning Methods","A platform for predicting failures in a smart home is presented, including a detailed methodology and algorithms for integrating a fault-prediction module into the overall smart-home system. The approach uses machine learning to analyze large data streams from IoT devices and sensors, detect patterns preceding malfunctions, and trigger proactive responses. Preventive actions include automatically adjusting device operation and performing backups based on predicted risk, improving reliability and security.","Methodology for Predicting Failures in a Smart Home based on Machine Learning Methods  \nViktoriia Zhebka1, Pavlo Skladannyi2, Serhii Zhebka1, Svitlana Shlianchak3, and Andrii Bondarchuk1  \n1 State University of Information and Communication Technologies, 7 Solomenskaya str., Kyiv, 03110, Ukraine  \n2 Borys Grinchenko Kyiv Metropolitan University, 18/2 Bulvarno-Kudriavska str., Kyiv, 04053, Ukraine  \n3 Volodymyr Vynnychenko Central Ukrainian State University, 1 Shevchenka str., Kropyvnytskyi, 25006, Ukraine  \nAbstract  \nThe article presents a platform for predicting failures in a smart home. A detailed algorithm of the predicting platform has been described. An algorithm for integrating the fault prediction platform into the smart home system has been developed. An algorithm for the functioning of a smart home with a failure prediction program based on machine learning has been presented. The software has been developed using the JHipster1 generator and the Java programming language. The use of machine learning methods in a smart home system expands its ability to analyze large amounts of data and identify patterns that may precede failures. This allows the system to predict possible problems and respond to them in advance. The use of preventive measures allows the system to automatically take measures to avoid failures, such as automatically adjusting the operation of devices or performing backups based on predictions.  \nKeywords  \nFailures, machine learning methods, predicting, methodology, information technology, IoT, smart home.  \n1. Introduction  \nThe rise of smart homes, facilitated by advancements in the Internet of Things (IoT) and smart technologies, offers convenient automation and control over various household systems like lighting, heating, security, and energy efficiency [1] . Yet, with increased complexity comes a higher risk of malfunctions or issues [2] . Factors such as network instability, software glitches, and faulty devices can create unpredictable scenarios, compromising both the functionality and security of a smart home [3] .  \nAnticipating these failures has become a pertinent concern, prompting the application of machine learning techniques for prediction.  \nBy leveraging machine learning algorithms, it becomes feasible to sift through vast datasets,  \nidentifying deviations and trends indicative of impending failures. This proactive approach enables preemptive measures to mitigate potential problems before they manifest.  \nPresently, most smart home failure prediction relies on reactive analysis, meaning anomalies or failures are detected after they occur, making prevention challenging. However, employing machine learning methods such as classification, clustering, and prediction algorithms holds promise in developing systems capable of forecasting failures in advance.  \nThese systems utilize data from various sources including sensors, IoT devices, energy consumption records, etc., to discern patterns and anomalies preceding disruptions [4, 5] . Armed with this insight, machine learning systems construct predictive models that  \nrespond to specific signals or deviations from  \nCPITS-2024: Cybersecurity Providing in Information and Telecommunication Systems, February 28, 2024, Kyiv, Ukraine  \n[EMAIL: viktoria_zhebka@ukr.net](EMAIL: viktoria_zhebka@ukr.net) (V. Zhebka); [p.skladannyi@kubg.edu.ua](p.skladannyi@kubg.edu.ua) (P. Skladannyi); [szhebka@hotmail.com](szhebka@hotmail.com) (S. Zhebka);  \n[shlanchaksveta@gmail.com](shlanchaksveta@gmail.com) (S. Shlianchak); [dekan.it@ukr.net](dekan.it@ukr.net) (A. Bondarchuk)  \nORCID: 0000-0003-4051-1190 (V. Zhebka); 0000-0002-7775-6039 (P. Skladannyi); 0009-0007-4620-9888 (S. Zhebka); 0000-0001-9893- 5709 (S. Shlianchak); 0000-0001-5124-5102 (A. Bondarchuk)  \n©️ 2024 Copyright for this paper by its authors.  \nUse permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).  \nCEUR Workshop Proceedings ([CEUR-WS.org](CEUR-WS.org))  \nCEUR ~~","cbCaic7Jmu2RXGLA","https://ap.wps.com/l/cbCaic7Jmu2RXGLA","pdf",780935,1,11,"English","en",105,"# Introduction\n# Research Results\n## Failure prediction platform architecture\n## Data sources and IoT integration","[{\"question\":\"Why is failure prediction important in smart homes?\",\"answer\":\"Smart homes combine many automated subsystems, and increased complexity raises the risk of malfunctions. Network instability, software glitches, and faulty devices can reduce both functionality and security, so earlier detection improves prevention.\"},{\"question\":\"What machine learning methods are used for failure prediction?\",\"answer\":\"The methodology discusses using machine learning for forecasting failures by analyzing deviations and trends in data. It highlights classification, clustering, and prediction algorithms as promising options for proactive detection.\"},{\"question\":\"How is the failure prediction platform integrated with the smart home system?\",\"answer\":\"The document provides an algorithm for integrating a fault prediction platform into the smart home, based on an IoT platform that handles operational and historical data. It supports real-time processing and later storage/analysis to drive alerts and corrective actions.\"}]","Methodology for Predicting Failures in a Smart Home based on Machine Learning Methods | PDF",1785733015,28,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":28},"methodology-for-predicting-failures-in-a-smart-home-based-on-machine-learning-methods","",{"@graph":36,"@context":85},[37,54,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/methodology-for-predicting-failures-in-a-smart-home-based-on-machine-learning-methods/120953/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-03",true,{"@type":65,"interactionType":66,"userInteractionCount":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"Why is failure prediction important in smart homes?","Question",{"text":75,"@type":76},"Smart homes combine many automated subsystems, and increased complexity raises the risk of malfunctions. Network instability, software glitches, and faulty devices can reduce both functionality and security, so earlier detection improves prevention.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"What machine learning methods are used for failure prediction?",{"text":80,"@type":76},"The methodology discusses using machine learning for forecasting failures by analyzing deviations and trends in data. It highlights classification, clustering, and prediction algorithms as promising options for proactive detection.",{"name":82,"@type":73,"acceptedAnswer":83},"How is the failure prediction platform integrated with the smart home system?",{"text":84,"@type":76},"The document provides an algorithm for integrating a fault prediction platform into the smart home, based on an IoT platform that handles operational and historical data. It supports real-time processing and later storage/analysis to drive alerts and corrective actions.","https://schema.org",{"og:url":52,"og:type":87,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":89,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":92},[93,97,101,105,110,115,120,123,128,131,135],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":94,"show_sort_weight":95,"slug":96},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":98,"show_sort_weight":99,"slug":100},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":102,"show_sort_weight":103,"slug":104},"Exam",70,"exam",{"id":106,"doc_module":4,"doc_module_name":46,"category_name":107,"show_sort_weight":108,"slug":109},5,"Comic",60,"comic",{"id":111,"doc_module":4,"doc_module_name":46,"category_name":112,"show_sort_weight":113,"slug":114},6,"Technology",50,"technology",{"id":116,"doc_module":4,"doc_module_name":46,"category_name":117,"show_sort_weight":118,"slug":119},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":121,"slug":122},30,"research-report",{"id":124,"doc_module":4,"doc_module_name":46,"category_name":125,"show_sort_weight":126,"slug":127},9,"Religion & Spirituality",20,"religion-spirituality",{"id":126,"doc_module":4,"doc_module_name":46,"category_name":129,"show_sort_weight":126,"slug":130},"World Cup","world-cup",{"id":132,"doc_module":4,"doc_module_name":46,"category_name":133,"show_sort_weight":132,"slug":134},10,"Lifestyle","lifestyle",{"id":136,"doc_module":4,"doc_module_name":46,"category_name":137,"show_sort_weight":106,"slug":138},19,"General","general"]