[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-124138-en":3,"doc-seo-124138-105":29,"detail-sidebar-cat-0-en-105":90},{"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":11,"language":21,"language_code":22,"site_id":23,"html_lang":22,"table_of_contents":24,"faqs":25,"seo_title":26,"seo_description":14,"update_tm":27,"read_time":28},124138,7971461740909,"Levi","https://ap-avatar.wpscdn.com/davatar_155a257f0dc6eb9ab79c44ca47cae57d",8,"Research & Report","Indoor Localization with Ensemble Machine Learning via Visible Light Communication Channels - Article 16","An indoor localization system based on received signal strength, visible light communication (VLC), and several machine learning approaches is proposed to improve indoor positioning reliability. The framework is structured into two phases: dataset creation using MATLAB to build an indoor VLC channel model, followed by training and evaluation with ensemble machine learning models including random forest, decision tree, and gradient boosting. Robustness is assessed using training/testing time and multiple metrics such as classification accuracy, AUC, F1-score, precision, recall, logloss, and specificity, plus error metrics like MSE, RMSE, MAE, and CVRMSE.","Journal of Engineering Research  \n\n| Volume 8\u003Cbr>Issue 6 issue 6 | Article 16 |\n| --- | --- |\n| 2024\u003Cbr>Indoor Localization with Ensemble Machine Learning via Visible Light Communication Channels\u003Cbr>Alzahraa M. Ghonim\u003Cbr>Mechatronics Engineering Department, High Institute of Engineering and Technology (HIET), ElMahala Elkobra, Egypt, [alzahraaghonim80@gmail.com](alzahraaghonim80@gmail.com)\u003Cbr>Wessam M. Salama\u003Cbr>Department of Computer Engineering, Faculty of Engineering, Pharos University in Alexandria, Canal El Mahmoudia Street, Beside Green Plaza Complex 21648, Alexandria, Egypt., [wessam.salama@pua.edu.eg](wessam.salama@pua.edu.eg)\u003Cbr>Follow this and additional works at: [https://digitalcommons.aaru.edu.jo/erjeng](https://digitalcommons.aaru.edu.jo/erjeng)\u003Cbr> Part of the Other Computer Engineering Commons, and the Systems and Communications Commons |  |\n\nRecommended Citation  \nGhonim, Alzahraa M. and Salama, Wessam M. (2024) \"Indoor Localization with Ensemble Machine Learning via Visible Light Communication Channels,\" Journal of Engineering Research: Vol. 8: Iss. 6, Article 16.  \nDOI: 10.70259/engJER.2024.861763  \nAvailable at: [https://digitalcommons.aaru.edu.jo/erjeng/vol8/iss6/16](https://digitalcommons.aaru.edu.jo/erjeng/vol8/iss6/16)  \nThis Article is brought to you for free and open access by Arab Journals Platform. It has been accepted for inclusion in Journal of Engineering Research by an authorized editor. The journal is hosted on Digital Commons, an Elsevier platform. For more information, [please contact marah@aaru.edu.jo](please contact marah@aaru.edu.jo), [rakan@aaru.edu.jo](rakan@aaru.edu.jo) .  \nIndoor Localization with Ensemble Machine Learning via Visible Light Communication Channels  \nAlzahraa M. Ghonim 1*,Wessam M. Salama2,  \n1 Mechatronics Engineering Department, High Institute of Engineering and Technology (HIET), ElMahala Elkobra, Egypt  \n2 Department of Basic Science, Faculty of Engineering, Pharos University, Alexandria, Egypt  \n*Corresponding author’s email: [Alzahraaghonim80@gmail.com](Alzahraaghonim80@gmail.com)  \nAbstract-An indoor localization system based on received signal strength, visible light communication (VLC) and several machine learning approaches is proposed in this paper. Our proposed framework is divided into two strategies. The first one is consisting of gathering our dataset based on MATLAB software to create indoor VLC channel model. While the second phase is depending on training the gained dataset using ensemble machine learning models. Specifically, random forest, decision tree and gradient boosting models. In order to evaluate the robustness of the proposed framework, several evaluation metrics are applied, specifically, training time, testing time, classification accuracy (CA), area under curve (AUC), F1-score, precision, recall, logloss, and specificity.  \nIt is observed that the proposed framework achieves 􀫙 . 􀫢􀫢􀫚 for AUC, 􀫙 . 􀫢􀫡􀫟 for CA, F1-score, precision and recall. While logloss, and specificity obtains 􀫙 . 􀫙􀫠􀫠 and 􀫙 . 􀫢􀫢􀫚 , respectively. Moreover, additional several evaluation error metrics are applied, mean square error (MSE), root mean square error (RMSE), mean absolute error (MAE) and coefficient of derivation of RMSE (CVRMSE). The gained results are 0.003 for MSE, RMSE is 􀫙 . 􀫙􀫞􀫞 , MAE is 􀫙 . 􀫙􀫝􀫚 and CVRMSE is 􀫚 . 􀫜􀫚􀫟 . Our proposed framework achieves the best performance based on different evaluation metrics.  \nKeywords- Visible light communication; Localization; Ensable machine learning; Decision tree ; Random forest; Gradient boost.  \nI. INTRODUCTION  \nTracking technologies indoor environments have become the backbone of various industries and sectors. Their real-time tracking capabilities empower us with enhanced security, emergency response, and asset management. By recognizing and analyzing activities, we unlock valuable insights that drive healthcare advancements and workplace productivity. Furthermore, navigation services become seamless, emergencies are","cbCaieLvtucNLVuE","https://ap.wps.com/l/cbCaieLvtucNLVuE","pdf",664687,1,"English","en",105,"# I. Introduction\n## Indoor tracking needs and conventional technologies\n## Optical wireless and VLC for localization\n## Motivation for AI/ML-based solutions","[{\"question\":\"How is the proposed indoor localization framework organized?\",\"answer\":\"It uses two phases: building an indoor VLC channel dataset with MATLAB, then training ensemble machine learning models on the gained dataset.\"},{\"question\":\"Which machine learning models are evaluated for localization?\",\"answer\":\"The study trains and evaluates random forest, decision tree, and gradient boosting models as ensemble machine learning approaches.\"},{\"question\":\"What evaluation metrics are used to assess performance?\",\"answer\":\"Performance is measured with classification accuracy, AUC, F1-score, precision, recall, logloss, specificity, and additional error metrics including MSE, RMSE, MAE, and CVRMSE.\"}]","Indoor Localization with Ensemble Machine Learning via Visible Light Communication Channels - Article 16 | PDF",1785820663,20,{"code":4,"msg":30,"data":31},"ok",{"site_id":23,"language":22,"slug":32,"title":13,"keywords":33,"description":14,"schema_data":34,"social_meta":85,"head_meta":87,"extra_data":89,"updated_unix":27},"indoor-localization-with-ensemble-machine-learning-via-visible-light-communication-channels-article-16","",{"@graph":35,"@context":84},[36,53,67],{"@type":37,"itemListElement":38},"BreadcrumbList",[39,43,47,50],{"item":40,"name":41,"@type":42,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":44,"name":45,"@type":42,"position":46},"https://docshare.wps.com/document/","Document",2,{"item":48,"name":12,"@type":42,"position":49},"https://docshare.wps.com/document/research-report/",3,{"item":51,"name":13,"@type":42,"position":52},"https://docshare.wps.com/document/indoor-localization-with-ensemble-machine-learning-via-visible-light-communication-channels-article-16/124138/",4,{"url":51,"name":13,"@type":54,"author":55,"headline":13,"publisher":57,"fileFormat":60,"inLanguage":22,"description":14,"dateModified":61,"datePublished":61,"encodingFormat":60,"isAccessibleForFree":62,"interactionStatistic":63},"DigitalDocument",{"name":9,"@type":56},"Person",{"url":40,"name":58,"@type":59},"DocShare","Organization","application/pdf","2026-08-04",true,{"@type":64,"interactionType":65,"userInteractionCount":4},"InteractionCounter",{"@type":66},"ViewAction",{"@type":68,"mainEntity":69},"FAQPage",[70,76,80],{"name":71,"@type":72,"acceptedAnswer":73},"How is the proposed indoor localization framework organized?","Question",{"text":74,"@type":75},"It uses two phases: building an indoor VLC channel dataset with MATLAB, then training ensemble machine learning models on the gained dataset.","Answer",{"name":77,"@type":72,"acceptedAnswer":78},"Which machine learning models are evaluated for localization?",{"text":79,"@type":75},"The study trains and evaluates random forest, decision tree, and gradient boosting models as ensemble machine learning approaches.",{"name":81,"@type":72,"acceptedAnswer":82},"What evaluation metrics are used to assess performance?",{"text":83,"@type":75},"Performance is measured with classification accuracy, AUC, F1-score, precision, recall, logloss, specificity, and additional error metrics including MSE, RMSE, MAE, and CVRMSE.","https://schema.org",{"og:url":51,"og:type":86,"og:title":13,"og:site_name":58,"og:description":14},"article",{"robots":88,"canonical":51},"index,follow",{"doc_id":7,"site_id":23},{"code":4,"msg":5,"data":91},[92,96,100,104,109,114,119,122,126,129,133],{"id":20,"doc_module":4,"doc_module_name":45,"category_name":93,"show_sort_weight":94,"slug":95},"Story & Novel",90,"story-novel",{"id":46,"doc_module":4,"doc_module_name":45,"category_name":97,"show_sort_weight":98,"slug":99},"Literature",80,"literature",{"id":52,"doc_module":4,"doc_module_name":45,"category_name":101,"show_sort_weight":102,"slug":103},"Exam",70,"exam",{"id":105,"doc_module":4,"doc_module_name":45,"category_name":106,"show_sort_weight":107,"slug":108},5,"Comic",60,"comic",{"id":110,"doc_module":4,"doc_module_name":45,"category_name":111,"show_sort_weight":112,"slug":113},6,"Technology",50,"technology",{"id":115,"doc_module":4,"doc_module_name":45,"category_name":116,"show_sort_weight":117,"slug":118},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":45,"category_name":12,"show_sort_weight":120,"slug":121},30,"research-report",{"id":123,"doc_module":4,"doc_module_name":45,"category_name":124,"show_sort_weight":28,"slug":125},9,"Religion & Spirituality","religion-spirituality",{"id":28,"doc_module":4,"doc_module_name":45,"category_name":127,"show_sort_weight":28,"slug":128},"World Cup","world-cup",{"id":130,"doc_module":4,"doc_module_name":45,"category_name":131,"show_sort_weight":130,"slug":132},10,"Lifestyle","lifestyle",{"id":134,"doc_module":4,"doc_module_name":45,"category_name":135,"show_sort_weight":105,"slug":136},19,"General","general"]