[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-123329-en":3,"doc-seo-123329-105":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},123329,1374391974468,"Eden","https://ap-avatar.wpscdn.com/davatar_29158cc5080c5b710cf443261637dec0",8,"Research & Report","SNR and RSSI Based an Optimized Machine Learning Based Indoor Localization Approach - Multistory Round Building Scenario over LoRa Network","Localization is essential when a machine’s exact position is unknown, particularly in complex indoor environments where signal disruptions and obstacles reduce reliability. This research improves indoor position prediction over long-range LoRa networks using an optimized machine learning approach. Fingerprinting-based data are collected across different multistory round building positions, with RSSI recorded alongside SNR while considering noise factors. The work evaluates reference point accuracy using a modified KNN method (MKNN), showing superior performance in accuracy and computational complexity versus competing algorithms.","Tech Science Press  \nDOI: 10.32604/cmc.2024.052169  \nARTICLE  \nSNR and RSSI Based an Optimized Machine Learning Based Indoor Localization Approach: Multistory Round Building Scenario over LoRa Network  \nMuhammad Ayoub Kamal1 , 3 , Muhammad Mansoor Alam1 , 2 ,4 , 6 , Aznida Abu Bakar Sajak1 and Mazliham Mohd Su’ud2 , 5 , *  \n1Malaysian Institute of Information Technology (MIIT), Universiti Kuala Lumpur, Kuala Lumpur, 50250, Malaysia 2 Faculty of Computing and Informatics, Multimedia University, Cyberjaya, 63100, Malaysia  \n3 Depertment of Computer Science, DHA Suffa University, Karachi, Sindh, 75500, Pakistan  \n4 Riphah Institute of System Engineering (RISE), Faculty of Computing, Riphah International University, Islamabad, 46000, Pakistan  \n5Malaysian France Institute (MFI), Universiti Kuala Lumpur, Kuala Lumpur, 50250, Malaysia  \n6 Faculty of Engineering and Information Technology, School of Computer Science, University of Technology Sydney, Ultimo, NSW 2007, Australia  \n*Corresponding Author: Mazliham Mohd Su’[ud. Email: mazliham@mmu.edu.my](ud. Email: mazliham@mmu.edu.my)[ ](ud. Email: mazliham@mmu.edu.my)[Received: 25 March 2024 Accepted: 23 May 2024 Published: 15 August 2024](Received: 25 March 2024 Accepted: 23 May 2024 Published: 15 August 2024)  \nABSTRACT  \nIn situations when the precise position of a machine is unknown, localization becomes crucial. This research focuses on improving the position prediction accuracy over long-range (LoRa) network using an optimized machine learning-based technique. In order to increase the prediction accuracy of the reference point position on the data collected using the fingerprinting method over LoRa technology, this study proposed an optimized machine learning(ML)based algorithm. Received signal strength indicator(RSSI)data from the sensors at different positions was first gathered via an experiment through the LoRa network in a multistory round layout building. The noise factor is also taken into account, and the signal-to-noise ratio (SNR) value is recorded for every RSSI measurement. This study concludes the examination of reference point accuracy with the modified KNN method (MKNN). MKNN was created to more precisely anticipate the position of the reference point. The findings showed that MKNN outperformed other algorithms in terms of accuracy and complexity.  \nKEYWORDS  \nIndoor localization; MKNN; LoRa; machine learning; classification; RSSI; SNR; localization  \n1 Introduction  \nLocalization is the process of determining an object’s position or location [1] . Positioning and geo-referencing explain how to locate the location. Improving the localization accuracy offered by different IoT devices is one of the most significant research issue nowadays, especially at hazardous and difficult-to-reach worksites with varying surroundings [2] . Localization is crucial for developing  \nThis work is licensed under a Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.  \n1928 CMC, 2024, vol. 80, no.2  \nInternet of Things (IoT) solutions for smart cities that allow devices to be tracked or seen both inside and outside of buildings [3] . Global Positioning System (GPS) is commonly used for outdoor location applications [4] . However, GPS is not appropriate for inside localization services because of the significant and dense obstacles that come with structures as well as the increased power consumption [5] . The availability of location data is becoming a more and more necessary component for current communication infrastructure in order to provide location-based services. However, the GPS and mobile phone networks encounter their most serious problems in indoor environmentsand in scenarios with strong shadowing effects, when there is a disruption in satellite or mobile signal [6] .  \nTherefore, in order to accurately estimate the location of the un-located node, f","cbCaijI72W9w2UYv","https://ap.wps.com/l/cbCaijI72W9w2UYv","pdf",1565508,1,19,"English","en",105,"# Introduction\n## Localization in indoor environments\n## Limitations of GPS and mobile networks\n## LOS/NLOS deployment and attenuation\n## RSSI-based distance estimation\n## Role of SNR and noise processing\n## LPWAN and RSS-driven localization\n# Proposed approach and evaluation","[{\"question\":\"Why is indoor localization important in this research?\",\"answer\":\"Indoor localization is crucial when a machine’s position is unknown, especially in challenging settings with obstacles and signal disruptions that make conventional outdoor methods unreliable.\"},{\"question\":\"How are RSSI and SNR used for positioning?\",\"answer\":\"RSSI measurements are collected at different positions, and SNR values are recorded for each RSSI while accounting for noise, improving the quality of the fingerprinting data used for prediction.\"},{\"question\":\"What method is used to enhance reference point accuracy?\",\"answer\":\"The study uses a modified KNN method (MKNN), designed to predict the reference point position more precisely, achieving better accuracy and complexity than other algorithms.\"}]","SNR and RSSI Based an Optimized Machine Learning Based Indoor Localization Approach - 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