{"id":9732,"date":"2025-02-12T09:49:27","date_gmt":"2025-02-12T09:49:27","guid":{"rendered":"https:\/\/www.beaconzone.co.uk\/blog\/?p=9732"},"modified":"2025-02-12T09:49:28","modified_gmt":"2025-02-12T09:49:28","slug":"using-support-vector-regression-svr-with-beacons","status":"publish","type":"post","link":"https:\/\/www.beaconzone.co.uk\/blog\/using-support-vector-regression-svr-with-beacons\/","title":{"rendered":"Using Support Vector Regression (SVR) with Beacons"},"content":{"rendered":"\n<p>A <a href=\"https:\/\/www.researchgate.net\/profile\/Hemani-Kaushal\/publication\/387542022_Experimental_optimization_of_BLE_beacon-based_indoor_positioning_using_support_vector_regression\/links\/67738472e74ca64e1f3e2ac1\/Experimental-optimization-of-BLE-beacon-based-indoor-positioning-using-support-vector-regression.pdf\" target=\"_blank\" rel=\"noreferrer noopener\">new study<\/a> (pdf) explores optimising Bluetooth Low Energy (BLE) beacon-based indoor positioning systems using support vector regression (SVR). It addresses the challenge of accurately identifying building occupants&#8217; locations in real time, a critical requirement for applications such as emergency evacuations and asset tracking. Traditional methods, including trilateration and RSSI-based techniques, can face limitations like signal interference and non-line-of-sight issues.<\/p>\n\n\n<div class=\"wp-block-image\">\n<figure class=\"aligncenter size-full\"><img loading=\"lazy\" decoding=\"async\" width=\"469\" height=\"301\" src=\"https:\/\/www.beaconzone.co.uk\/blog\/wp-content\/uploads\/2025\/01\/svrandbeacons.jpg\" alt=\"\" class=\"wp-image-9735\" srcset=\"https:\/\/www.beaconzone.co.uk\/blog\/wp-content\/uploads\/2025\/01\/svrandbeacons.jpg 469w, https:\/\/www.beaconzone.co.uk\/blog\/wp-content\/uploads\/2025\/01\/svrandbeacons-300x193.jpg 300w\" sizes=\"(max-width: 469px) 85vw, 469px\" \/><\/figure><\/div>\n\n\n<p>The research adopts a fingerprinting method that uses pre-trained SVR models to improve positioning accuracy. BLE beacons, which are cost-effective and energy-efficient, were deployed across a controlled environment, and extensive RSSI data was collected and pre-processed. The model&#8217;s hyperparameters were fine-tuned to achieve optimal performance. Experimental results demonstrated a significant improvement in accuracy, with the lowest root mean squared error (RMSE) recorded as 0.9168 feet.<\/p>\n\n\n<div class=\"wp-block-image\">\n<figure class=\"aligncenter size-full is-resized\"><img loading=\"lazy\" decoding=\"async\" width=\"900\" height=\"565\" src=\"https:\/\/www.beaconzone.co.uk\/blog\/wp-content\/uploads\/2025\/01\/beacondeployment.jpg\" alt=\"\" class=\"wp-image-9736\" style=\"width:666px;height:auto\" srcset=\"https:\/\/www.beaconzone.co.uk\/blog\/wp-content\/uploads\/2025\/01\/beacondeployment.jpg 900w, https:\/\/www.beaconzone.co.uk\/blog\/wp-content\/uploads\/2025\/01\/beacondeployment-300x188.jpg 300w, https:\/\/www.beaconzone.co.uk\/blog\/wp-content\/uploads\/2025\/01\/beacondeployment-768x482.jpg 768w\" sizes=\"(max-width: 709px) 85vw, (max-width: 909px) 67vw, (max-width: 1362px) 62vw, 840px\" \/><\/figure><\/div>\n\n\n<p>The findings underscore the potential of machine learning, particularly SVR, in enhancing the reliability of indoor positioning systems. This study provides a benchmark for future research, highlighting its practical applications in emergency scenarios and the advantages of BLE technology in such implementations.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>A new study (pdf) explores optimising Bluetooth Low Energy (BLE) beacon-based indoor positioning systems using support vector regression (SVR). It addresses the challenge of accurately identifying building occupants&#8217; locations in real time, a critical requirement for applications such as emergency evacuations and asset tracking. Traditional methods, including trilateration and RSSI-based techniques, can face limitations like &hellip; <a href=\"https:\/\/www.beaconzone.co.uk\/blog\/using-support-vector-regression-svr-with-beacons\/\" class=\"more-link\">Continue reading<span class=\"screen-reader-text\"> &#8220;Using Support Vector Regression (SVR) with Beacons&#8221;<\/span><\/a><\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[101,31,173],"tags":[],"_links":{"self":[{"href":"https:\/\/www.beaconzone.co.uk\/blog\/wp-json\/wp\/v2\/posts\/9732"}],"collection":[{"href":"https:\/\/www.beaconzone.co.uk\/blog\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.beaconzone.co.uk\/blog\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.beaconzone.co.uk\/blog\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/www.beaconzone.co.uk\/blog\/wp-json\/wp\/v2\/comments?post=9732"}],"version-history":[{"count":3,"href":"https:\/\/www.beaconzone.co.uk\/blog\/wp-json\/wp\/v2\/posts\/9732\/revisions"}],"predecessor-version":[{"id":9737,"href":"https:\/\/www.beaconzone.co.uk\/blog\/wp-json\/wp\/v2\/posts\/9732\/revisions\/9737"}],"wp:attachment":[{"href":"https:\/\/www.beaconzone.co.uk\/blog\/wp-json\/wp\/v2\/media?parent=9732"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.beaconzone.co.uk\/blog\/wp-json\/wp\/v2\/categories?post=9732"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.beaconzone.co.uk\/blog\/wp-json\/wp\/v2\/tags?post=9732"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}