{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,28]],"date-time":"2025-10-28T03:18:28Z","timestamp":1761621508162,"version":"3.41.0"},"reference-count":64,"publisher":"Association for Computing Machinery (ACM)","issue":"1","license":[{"start":{"date-parts":[[2020,3,18]],"date-time":"2020-03-18T00:00:00Z","timestamp":1584489600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"funder":[{"DOI":"10.13039\/100006785","name":"Google Inc.","doi-asserted-by":"crossref","id":[{"id":"10.13039\/100006785","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["Proc. ACM Interact. Mob. Wearable Ubiquitous Technol."],"published-print":{"date-parts":[[2020,3,18]]},"abstract":"<jats:p>Due to the recent proliferation of location-based services indoors, the need for an accurate floor estimation technique that is easy to deploy in any typical multi-story building is higher than ever. Current approaches that attempt to solve the floor localization problem include sensor-based systems and 3D fingerprinting. Nevertheless, these systems incur high deployment and maintenance overhead, suffer from sensor drift and calibration issues, and\/or are not available to all users.<\/jats:p>\n          <jats:p>In this paper, we propose StoryTeller, a deep learning-based technique for floor prediction in multi-story buildings. StoryTeller leverages the ubiquitous WiFi signals to generate images that are input to a Convolutional Neural Network (CNN) which is trained to predict loors based on detected patterns in visible WiFi scans. Input images are created such that they capture the current WiFi-scan in an AP-independent manner. In addition, a novel virtual building concept is used to normalize the information in order to make them building-independent. This allows StoryTeller to reuse a trained network for a completely new building, significantly reducing the deployment overhead.<\/jats:p>\n          <jats:p>We have implemented and evaluated StoryTeller using three different buildings with a side-by-side comparison with the state-of-the-art floor estimation techniques. The results show that StoryTeller can estimate the user's floor at least 98.3% within one floor of the actual ground truth floor. This accuracy is consistent across the different testbeds and for scenarios where the models used were trained in a completely different building than the tested building. This highlights StoryTeller's ability to generalize to new buildings and its promise as a scalable, low-overhead, high-accuracy floor localization system.<\/jats:p>","DOI":"10.1145\/3380979","type":"journal-article","created":{"date-parts":[[2020,3,18]],"date-time":"2020-03-18T18:54:31Z","timestamp":1584557671000},"page":"1-20","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":26,"title":["The StoryTeller"],"prefix":"10.1145","volume":"4","author":[{"given":"Rizanne","family":"Elbakly","sequence":"first","affiliation":[{"name":"Egypt-Japan Univ. of Science and Technology (E-JUST), Egypt"}]},{"given":"Moustafa","family":"Youssef","sequence":"additional","affiliation":[{"name":"Alexandria University, Egypt"}]}],"member":"320","published-online":{"date-parts":[[2020,3,18]]},"reference":[{"key":"e_1_2_2_1_1","unstructured":"2019. Google Colab. https:\/\/colab.research.google.com  2019. 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