{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/low-effort-place-recognition-with-wifi","title":"Low-effort place recognition with WiFi fingerprints using deep learning","arxiv_id":"1611.02049","date":"2016-11-07","proceeding":null,"authors":["Michał Nowicki","Jan Wietrzykowski"],"abstract":"Using WiFi signals for indoor localization is the main localization modality\nof the existing personal indoor localization systems operating on mobile\ndevices. WiFi fingerprinting is also used for mobile robots, as WiFi signals\nare usually available indoors and can provide rough initial position estimate\nor can be used together with other positioning systems. Currently, the best\nsolutions rely on filtering, manual data analysis, and time-consuming parameter\ntuning to achieve reliable and accurate localization. In this work, we propose\nto use deep neural networks to significantly lower the work-force burden of the\nlocalization system design, while still achieving satisfactory results.\nAssuming the state-of-the-art hierarchical approach, we employ the DNN system\nfor building/floor classification. We show that stacked autoencoders allow to\nefficiently reduce the feature space in order to achieve robust and precise\nclassification. The proposed architecture is verified on the publicly available\nUJIIndoorLoc dataset and the results are compared with other solutions.","url_abs":"http://arxiv.org/abs/1611.02049v2","url_pdf":"http://arxiv.org/pdf/1611.02049v2.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"low-effort-place-recognition-with-wifi","repo_url":"https://github.com/kyeongsoo/indoor_localization","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":null},{"paper_slug":"low-effort-place-recognition-with-wifi","repo_url":"https://github.com/vohoaiviet/indoor_localization","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":null}],"tasks":[{"task_slug":"deep-learning","task_name":"Deep Learning"},{"task_slug":"classification","task_name":"General Classification"},{"task_slug":"indoor-localization","task_name":"Indoor Localization"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}