{"id":9254,"date":"2024-04-16T08:40:10","date_gmt":"2024-04-16T08:40:10","guid":{"rendered":"https:\/\/www.beaconzone.co.uk\/blog\/?p=9254"},"modified":"2024-04-19T07:49:10","modified_gmt":"2024-04-19T07:49:10","slug":"crowdsensing-proximity-detection","status":"publish","type":"post","link":"https:\/\/www.beaconzone.co.uk\/blog\/crowdsensing-proximity-detection\/","title":{"rendered":"Crowdsensing Proximity Detection"},"content":{"rendered":"\n

There’s a new study<\/a> on the performance of a proximity detection system for visitors in indoor museums using a Crowdsensing-based technique, authored by Michele Girolami, Davide La Rosa, and Paolo Barsocchi. This approach uses Bluetooth beacon data collected from visitors’ smartphones to calibrate two proximity detection algorithms: a range-based and a learning-based algorithm, embedded within a museum visiting application tested in the Monumental Cemetery’s museum in Pisa, Italy. <\/p>\n\n\n

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The experimental results demonstrate a significant improvement in performance when using crowd-sourced data, with accuracy metrics showing up to a 30% improvement compared to state-of-the-art algorithms. The research introduces a novel contribution by employing a Crowdsensing approach to improve the accuracy of proximity detection algorithms in a challenging indoor environment.<\/p>\n\n\n

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The study provides a detailed experimental campaign, including the design of the mobile application named R-app, to assess the performance enhancements achieved through this innovative method. The authors conclude that integrating Crowdsensing techniques with proximity detection algorithms offers a promising solution for enhancing visitor experiences in cultural heritage contexts.<\/p>\n\n\n\n

The resultant collected data is also available.<\/a><\/p>\n\n\n\n

Read about Beacons in Events and Visitor Spaces<\/a><\/p>\n","protected":false},"excerpt":{"rendered":"

There’s a new study on the performance of a proximity detection system for visitors in indoor museums using a Crowdsensing-based technique, authored by Michele Girolami, Davide La Rosa, and Paolo Barsocchi. This approach uses Bluetooth beacon data collected from visitors’ smartphones to calibrate two proximity detection algorithms: a range-based and a learning-based algorithm, embedded within … Continue reading “Crowdsensing Proximity Detection”<\/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":[92,32,123],"tags":[],"_links":{"self":[{"href":"https:\/\/www.beaconzone.co.uk\/blog\/wp-json\/wp\/v2\/posts\/9254"}],"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=9254"}],"version-history":[{"count":8,"href":"https:\/\/www.beaconzone.co.uk\/blog\/wp-json\/wp\/v2\/posts\/9254\/revisions"}],"predecessor-version":[{"id":9329,"href":"https:\/\/www.beaconzone.co.uk\/blog\/wp-json\/wp\/v2\/posts\/9254\/revisions\/9329"}],"wp:attachment":[{"href":"https:\/\/www.beaconzone.co.uk\/blog\/wp-json\/wp\/v2\/media?parent=9254"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.beaconzone.co.uk\/blog\/wp-json\/wp\/v2\/categories?post=9254"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.beaconzone.co.uk\/blog\/wp-json\/wp\/v2\/tags?post=9254"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}