[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-123946-en":3,"doc-seo-123946-105":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},123946,5909877438554,"Maeve","https://ap-avatar.wpscdn.com/avatar/5600025385ad2bf12a7?_k=1778553567797529272",8,"Research & Report","Evaluation of an Indoor Location System Using Edge Computing and Machine Learning Algorithms","The paper evaluates precise indoor positioning using edge computing with off-the-shelf indoor devices for applications such as smart homes and smart health. Indoor environments still lack standardized, accurate methods, particularly under complex, multi-room conditions where received-signal-strength measurements suffer from multipath and line-of-sight variability. A low-cost system is designed with an ESP32 module and RSSI features, integrating embedded machine learning classifiers. Performance is assessed on four locations (bathroom, kitchen, bedroom, living room) using RF, DT, and SVM, transmitted via MQTT.","JOE International Journal of  \nOnline and Biomedical Engineering  \n[Onli](Online-Journals.org)[ne-Jo](Online-Journals.org)[urnals](Online-Journals.org)[.org](Online-Journals.org)  \niJOE | eISSN: 2626-8493 | Vol. 20 No. 4 (2024) |   \n[https://doi.org/10.3991/ijoe.v20i04.46771](https://doi.org/10.3991/ijoe.v20i04.46771)  \nPAPER  \nEvaluation of an Indoor Location System Using Edge Computing and Machine Learning Algorithms  \nRicardo Yauri(*), Rafael Espino, Antero Castro  \nUniversidad Tecnológica del Perú, Lima, Perú  \n[c24068@utp.edu.pe](c24068@utp.edu.pe)  \nABSTRACT  \nThe paper aims to evaluate precise location techniques with indoor devices using edge computing technologies, which are important for services such as smart homes and health. Despite their growing importance, indoor locations lack precise and standard methods, especially in complex environments. Solving this is being attempted through technologies such as reconfigurable surfaces and deep learning models, with attention to overcoming the challenges of indoor placement. The main objective of the study is to design a low-cost indoor location system using the ESP32 module and RSSI signals, integrated with embedded machine learning algorithms. The system to be developed will allow determining the location of objects or people with a location device through SSID signals from access points. The main objective is to evaluate the performance of three machine learning algorithms—random forest (RF), decision tree (DT) and support vector machine (SVM)—in the detection of four different locations (bathroom, kitchen, bedroom, and living room), involving the definition of system characteristics, data acquisition, the development of classifiers, and their integration in the ESP32 module to transmit location data wirelessly through the MQTT protocol. As a result of the evaluation, the DT model stands out for its efficiency under limited resource conditions during real-time implementation, but it may face challenges related to overfitting and resources atthe implementation stage.  \nKEYWORDS  \nlocalization, machine learning, edge computing, Wi-Fi  \n1 INTRODUCTION  \nCurrently, there are efforts focused on the precise location of nodes based on Internet of Things (IoT) technologies enabled for different communication networks,  \nYauri, R., Espino, R., Castro, A. (2024) . Evaluation of an Indoor Location System Using Edge Computing and Machine Learning Algorithms. International Journal of Online and Biomedical Engineering (iJOE), 20(4), pp. 4–17. [https://doi.org/10.3991/ijoe.v20i04.46771](https://doi.org/10.3991/ijoe.v20i04.46771)[ ](https://doi.org/10.3991/ijoe.v20i04.46771)[Article submitted 2023-11-15. Revision uploaded 2024-01-12. Final acceptance 2024-01-13.](Article submitted 2023-11-15. Revision uploaded 2024-01-12. Final acceptance 2024-01-13.)  \n© 2024 by the authors of this article. Published under CC-BY.  \n4 International Journal of Online and Biomedical Engineering (iJOE) iJOE | Vol. 20 No. 4 (2024)  \nEvaluation of an Indoor Location System Using Edge Computing and Machine Learning Algorithms  \nwhich is essential for a variety of location-based services, such as smart homes, smart healthcare, monitoring environments, personal navigation, and intelligent transportation. In addition, there is a lack of standardized and accurate positioning methods for indoor environments where mobile devices and IoT are currently being developed [1][2] .  \nUnlike outdoor positioning methods, where highly accurate techniques exist, equally reliable approaches are not available indoors. Indoor positioning, often based on WiFi fingerprints, although useful, still faces notable limitations that require more advanced and accurate solutions due to the lack of precision and generalization in indoor positioning systems that make use of WiFi fingerprints  \n[3] . When localization is needed indoors and in multi-building and multi-story environments, building and story classification (BFC) functionality","cbCaieA9OO5429xQ","https://ap.wps.com/l/cbCaieA9OO5429xQ","pdf",965616,1,14,"English","en",105,"# Introduction\n## Motivation for indoor localization\n## Limitations of Wi-Fi fingerprints and RSSI\n## Related work and prior techniques\n# System Design (ESP32 and RSSI-Based Features)\n## Data acquisition and feature definition\n## Classifier development and integration\n# Experimental Evaluation\n## Locations tested and model comparison\n## Real-time resource constraints\n# Results and Discussion","[{\"question\":\"What is the main objective of the study?\",\"answer\":\"To design a low-cost indoor location system using an ESP32 module with RSSI signals and embedded machine learning algorithms, and then evaluate it in real conditions.\"},{\"question\":\"Which machine learning algorithms are compared in the paper?\",\"answer\":\"Random Forest (RF), Decision Tree (DT), and Support Vector Machine (SVM) are evaluated for classifying four indoor locations.\"},{\"question\":\"How does the system communicate location data?\",\"answer\":\"The ESP32-based system transmits location results wirelessly using the MQTT protocol based on detected SSID signals from access points.\"}]","Evaluation of an Indoor Location System Using Edge Computing and Machine Learning Algorithms | 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is the main objective of the study?","Question",{"text":75,"@type":76},"To design a low-cost indoor location system using an ESP32 module with RSSI signals and embedded machine learning algorithms, and then evaluate it in real conditions.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"Which machine learning algorithms are compared in the paper?",{"text":80,"@type":76},"Random Forest (RF), Decision Tree (DT), and Support Vector Machine (SVM) are evaluated for classifying four indoor locations.",{"name":82,"@type":73,"acceptedAnswer":83},"How does the system communicate location data?",{"text":84,"@type":76},"The ESP32-based system transmits location results wirelessly using the MQTT protocol based on detected SSID signals from access 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