[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-127061-en":3,"doc-seo-127061-105":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},127061,962084931830,"Theodore","https://ap-avatar.wpscdn.com/davatar_a8503ba1806abce46bf441b54a3ca4cd",8,"Research & Report","Bluetooth beacons based indoor positioning in shopping malls - using machine learning","Bluetooth beacon technology enables low-cost indoor positioning and is well suited for shopping malls where GPS fails indoors due to signal attenuation by walls. Bluetooth beacons typically achieve under 4 meters accuracy, while machine learning improves localization beyond traditional filtering. This work provides indoor localization based on Bluetooth beacons using multiple ML techniques, training models to locate customers’ smartphone devices in malls. Results show Extra-Trees and k-neighbors classifiers achieve over 90% accuracy and support analysis of spatial-temporal human behavior.","Bluetooth beacons based indoor positioning in a shopping malls  \nusing machine learning  \nKamel Maaloul1, Brahim Lejdel2, Eliseo Clementini3 , Nedioui Med Abdelhamid2  \n1LABTHOP Laboratory, Department of Informatic, Faculty of Exact Sciences, University of El-Oued, El-Oued, Algeria 2Department of Informatic, Faculty of Exact Sciences, University of El-Oued, El-Oued, Algeria  \n3 Department of Industrial and Information Engineering and Economics, University of L’Aquila, L’Aquila, Italy  \n\n| Article history:\u003Cbr>Received Jun 4, 2022 Revised Aug 14, 2022 Accepted Oct 30, 2022 | The adoption of Bluetooth beacon technology demonstrates a broad interest in indoor positioning technology because of its low cost and ease of use. Bluetooth beacons usually have an accuracy of fewer than 4 meters. The use of machine learning (ML) leads to results with greater accuracy compared to using traditional filtering methods. In this paper, we provide indoor localization based on Bluetooth beacons using several different ML techniques. We used ML algorithms to locate customers' devices in shopping malls. The extra-trees classifier and k-neighbors classifier found the device with greater than 90% accuracy. Other algorithms were able to determine the location with less accuracy. The results also showed that Bluetooth technology is a valid solution to find the data used to analyze the spatialtemporal behavior of individuals.\u003Cbr>This is an open access article under the CC BY-SA license.\u003Cbr> |\n| --- | --- |\n| Keywords:\u003Cbr>Bluetooth beacons Extra-trees classifier Indoor localization Machine learning Smartphone sensors |  |\n\nCorresponding Author:  \nKamel Maaloul  \nLABTHOP Laboratory, Department of Informatic, Faculty of Exact Sciences, University of El-Oued El-Oued PB 789, El-Oued, Algeria  \n[Email: maaloul-kamel@univ-eloued.dz](Email: maaloul-kamel@univ-eloued.dz)  \nArticle Info ABSTRACT  \n1. INTRODUCTION  \nIndoor positioning methods are in great demand nowadays for accurate positioning [1] . The signals from the global positioning system (GPS) cannot accurately determine indoor locations. Because walls greatly impede signal strength, making it impossible for them to pass through tall buildings and move within structures. Indoor location can be determined through the development of indoor positioning technologies, such as radio frequency identification (RFID), wireless accuracy (WiFi), and Bluetooth [2] . The indoor positioning system helps determine information about the movement patterns of customers. It also offers advanced customer service, such as the most visited places for shoppers in the mall and the reorganization of public facilities [3] .  \nMarket visitors can be dealt with by a device that works on Bluetooth and WiFi instead of a guide. These technologies are the most widely used because of their low cost and high accuracy, and they are used in all smartphones. We use Bluetooth-compatible devices because of their generally small size, low battery consumption, and low cost. This is done by sending a globally unique identifier and then picking it up by a compatible application or operating system. Received signal strength indication (RSSI) measurements represent the relative quality of a received signal on a device. After accounting for potential antenna and cable level losses, the RSSI shows the power level received. The stronger the signal, the higher the RSSI value. The number that is nearer to zero when measured in negative numbers typically indicates a better signal. The position calculation is based on RSSI values [4] .  \nThe effectiveness of the indoor location system requires important things, including permanent and accurate identification of the location of the user, rapid identification of the site; and the ability to adapt to a changing and difficult environment [5] . The development of artificial intelligence algorithms has increased the handling of machine learning (ML) techniques. It also led to the growth of data to enhance the qu","cbCaireIP8hO2WFr","https://ap.wps.com/l/cbCaireIP8hO2WFr","pdf",566031,1,11,"English","en",105,"# Abstract\n# 1. 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