[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-120492-en":3,"doc-seo-120492-105":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},120492,13056703019662,"Evangeline","https://ap-avatar.wpscdn.com/avatar/be000253a8e92610077?_k=1778726343310543188",8,"Research & Report","Assessing fingerprinting and machine learning approaches for wireless indoor localization - Abstract","A comparative analysis evaluates fingerprinting and machine learning techniques for Bluetooth Low Energy (BLE)-based indoor localization. Two fingerprinting algorithms—fingerprint feature extraction (FPFE) and Bayesian estimation (BE)—are studied alongside machine learning methods including support vector regression (SVR), ensemble learning, and instance-based learning. Performance is examined under ideal and real-world conditions that differ in available training or fingerprint database. With abundant user-collected data, FPFE achieves mean error 0.50 m, while sparse-data settings show BE and SVR mean errors of 1.785 m and 1.965 m.","Indonesian Journal of Electrical Engineering and Computer Science  \nVol. 37, No. 3, March 2025, pp. 2021∼2031  \nISSN: 2502-4752, DOI: 10.11591/ijeecs.v37.i3.pp2021-2031 ❒ 2021  \n\n| Assessing fingerprinting and machine learning approaches for wireless indoor localization\u003Cbr>Azkario Rizky Pratama1 , Muhammad Evan Anindya Wahyuaji1 , Muhammad Fadhil Nur Hidayat1 ,\u003Cbr>Bimo Sunarfri Hantono1 , Nur Abdillah Siddiq2\u003Cbr>1Department of Electrical and Information Engineering, Universitas Gadjah Mada, Yogyakarta, Indonesia\u003Cbr>2Department of Nuclear Engineering and Engineering Physics, Universitas Gadjah Mada, Yogyakarta, Indonesia |  |  |\n| --- | --- | --- |\n| Article Info\u003Cbr>Article history:\u003Cbr>Received Apr 2, 2024 Revised Sep 29, 2024 Accepted Oct 7, 2024\u003Cbr>Keywords:\u003Cbr>Ambient intelligence Bayesian estimation Bluetooth low energy Fingerprint feature extraction\u003Cbr>Fingerprinting Indoor localization Machine learning |  | ABSTRACT\u003Cbr>This paper presents a comparative analysis of fingerprinting and machine learning techniques for bluetooth low energy (BLE)-based localization. Two fingerprinting algorithms, namely fingerprint feature extraction (FPFE) and Bayesian estimation (BE), along with various machine learning approaches including support vector regression (SVR), ensemble learning, and instance-based learning, are investigated. The selection of techniques depends on the availability of training data or the fingerprint database, explored in both ideal scenario and realworld scenario. In ideal scenario where the system administrator can collect fingerprint data through users’ devices, FPFE emerges as the preferred algorithm, achieving superior performance with a mean error of 0.50 m. In the context of real-world scenario, where data collection from multiple devices is limited, the system administrator may gather fingerprint data for localization using one ora few specific devices. Our experiments reveal that when there is a scarcity of fingerprint data, BE and SVR exhibit acceptable performance, reaching a mean error of 1.785 m and 1.965 m, respectively.\u003Cbr>This is an open access article under the CC BY-SA license. |\n| Corresponding Author: |  |  |\n| Azkario Rizky Pratama\u003Cbr>Department of Electrical and Information Engineering, Universitas Gadjah Mada Yogyakarta, Indonesia\u003Cbr>Email: [azkario@ugm.ac.id](azkario@ugm.ac.id) |  |  |\n\n1. INTRODUCTION  \nWireless positioning and localization techniques are crucial for various applications, including indoor navigation, asset tracking, and location-based services. However, one of the biggest challenges in achieving accurate localization in large deployments is terminal heterogeneity, such as the presence of smartphones from different brands in indoor environments [1] . While solutions have been proposed to address the localization of heterogeneous devices indoors, such as the development of more robust algorithms and standardization efforts, many researchers still struggle to achieve optimal localization performance [2] . In this research, we aim to investigate and evaluate different methods to understand their performance under varying conditions. This study seeks to provide valuable recommendations for researchers developing indoor localization systems (ILS) .  \nThere are essentially two prominent methods for localization: fingerprinting and machine learningbased approaches. Fingerprinting and machine learning methods each have unique strengths and weaknesses. Fingerprinting, a traditional technique, involves creating a reference database of pre-collected signal characteristics from known locations [3] . These fingerprints are then compared to the measured signal characteristics  \nto estimate device location. On the other hand, machine learning-based approaches generate a learning model trained on data, which can then predict device location using captured signal data. Data collection is a critical aspect of both techniques. Fingerprinting requires extensive site surveys to collect signal ch","cbCaiiLii31ylx0Y","https://ap.wps.com/l/cbCaiiLii31ylx0Y","pdf",2469839,1,11,"English","en",105,"# Abstract\n# Introduction\n## Localization methods: fingerprinting vs machine learning\n## Evaluation metrics and influencing factors\n## Prior work and motivation","[{\"question\":\"What localization technologies and approaches are compared in the paper?\",\"answer\":\"The paper compares BLE-based fingerprinting methods (FPFE and BE) with several machine learning approaches, including SVR, ensemble learning, and instance-based learning.\"},{\"question\":\"How does performance differ between ideal and real-world data conditions?\",\"answer\":\"In an ideal scenario with administrator-collected fingerprints from users' devices, FPFE is best with a mean error of 0.50 m. In real-world scenarios with limited multi-device data, BE and SVR still achieve acceptable mean errors of 1.785 m and 1.965 m respectively.\"},{\"question\":\"What role does fingerprint data availability play in selecting the technique?\",\"answer\":\"Technique choice depends on whether sufficient training data or a fingerprint database is available. When data is scarce, the study indicates BE and SVR are more suitable than relying on FPFE's stronger performance in data-rich settings.\"}]","Assessing fingerprinting and machine learning approaches for wireless indoor localization - Abstract | PDF",1785730348,28,{"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},"assessing-fingerprinting-and-machine-learning-approaches-for-wireless-indoor-localization-abstract","",{"@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/assessing-fingerprinting-and-machine-learning-approaches-for-wireless-indoor-localization-abstract/120492/",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},"What localization technologies and approaches are compared in the paper?","Question",{"text":75,"@type":76},"The paper compares BLE-based fingerprinting methods (FPFE and BE) with several machine learning approaches, including SVR, ensemble learning, and instance-based learning.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How does performance differ between ideal and real-world data conditions?",{"text":80,"@type":76},"In an ideal scenario with administrator-collected fingerprints from users' devices, FPFE is best with a mean error of 0.50 m. In real-world scenarios with limited multi-device data, BE and SVR still achieve acceptable mean errors of 1.785 m and 1.965 m respectively.",{"name":82,"@type":73,"acceptedAnswer":83},"What role does fingerprint data availability play in selecting the technique?",{"text":84,"@type":76},"Technique choice depends on whether sufficient training data or a fingerprint database is available. When data is scarce, the study indicates BE and SVR are more suitable than relying on FPFE's stronger performance in data-rich settings.","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"]