{"id":147135,"date":"2005-05-01T00:00:00","date_gmt":"2005-05-01T00:00:00","guid":{"rendered":"https:\/\/www.microsoft.com\/en-us\/research\/msr-research-item\/place-lab-device-positioning-using-radio-beacons-in-the-wild\/"},"modified":"2020-04-10T10:55:47","modified_gmt":"2020-04-10T17:55:47","slug":"place-lab-device-positioning-using-radio-beacons-in-the-wild","status":"publish","type":"msr-research-item","link":"https:\/\/www.microsoft.com\/en-us\/research\/publication\/place-lab-device-positioning-using-radio-beacons-in-the-wild\/","title":{"rendered":"Place Lab: Device Positioning Using Radio Beacons in the Wild"},"content":{"rendered":"<div class=\"asset-content\">\n<p>Location awareness is an important capability for mobile computing. Yet inexpensive, pervasive positioning\u2014a requirement for wide-scale adoption of location-aware computing\u2014has been elusive. We demonstrate a radio beacon-based approach to location, called Place Lab, that can overcome the lack of ubiquity and high-cost found in existing location sensing approaches. Using Place Lab, commodity laptops, PDAs and cell phones estimate their position by listening for the cell IDs of fixed radio beacons, such as wireless access points, and referencing the beacons\u2019 positions in a cached database. We present experimental results showing that 802.11 and GSM beacons are sufficiently pervasive in the greater Seattle area to achieve 20-30 meter median accuracy with nearly 100% coverage measured by availability in people\u2019s daily lives.<\/p>\n<\/div>\n<p><!-- .asset-content --><\/p>\n","protected":false},"excerpt":{"rendered":"<p>Location awareness is an important capability for mobile computing. Yet inexpensive, pervasive positioning\u2014a requirement for wide-scale adoption of location-aware computing\u2014has been elusive. We demonstrate a radio beacon-based approach to location, called Place Lab, that can overcome the lack of ubiquity and high-cost found in existing location sensing approaches. Using Place Lab, commodity laptops, PDAs and [&hellip;]<\/p>\n","protected":false},"featured_media":0,"template":"","meta":{"msr-url-field":"","msr-podcast-episode":"","msrModifiedDate":"","msrModifiedDateEnabled":false,"ep_exclude_from_search":false,"footnotes":""},"msr-content-type":[3],"msr-research-highlight":[],"research-area":[13554,13547],"msr-publication-type":[193716],"msr-product-type":[],"msr-focus-area":[],"msr-platform":[],"msr-download-source":[],"msr-locale":[268875],"msr-field-of-study":[],"msr-conference":[],"msr-journal":[],"msr-impact-theme":[],"msr-pillar":[],"class_list":["post-147135","msr-research-item","type-msr-research-item","status-publish","hentry","msr-research-area-human-computer-interaction","msr-research-area-systems-and-networking","msr-locale-en_us"],"msr_publishername":"Springer 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