{"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/a-scalable-deep-neural-network-architecture","title":"A Scalable Deep Neural Network Architecture for Multi-Building and Multi-Floor Indoor Localization Based on Wi-Fi Fingerprinting","arxiv_id":"1712.01990","date":"2017-12-06","proceeding":null,"authors":["Kyeong Soo Kim","Sanghyuk Lee","Kaizhu Huang"],"abstract":"One of the key technologies for future large-scale location-aware services\ncovering a complex of multi-story buildings --- e.g., a big shopping mall and a\nuniversity campus --- is a scalable indoor localization technique. In this\npaper, we report the current status of our investigation on the use of deep\nneural networks (DNNs) for scalable building/floor classification and\nfloor-level position estimation based on Wi-Fi fingerprinting. Exploiting the\nhierarchical nature of the building/floor estimation and floor-level\ncoordinates estimation of a location, we propose a new DNN architecture\nconsisting of a stacked autoencoder for the reduction of feature space\ndimension and a feed-forward classifier for multi-label classification of\nbuilding/floor/location, on which the multi-building and multi-floor indoor\nlocalization system based on Wi-Fi fingerprinting is built. Experimental\nresults for the performance of building/floor estimation and floor-level\ncoordinates estimation of a given location demonstrate the feasibility of the\nproposed DNN-based indoor localization system, which can provide near\nstate-of-the-art performance using a single DNN, for the implementation with\nlower complexity and energy consumption at mobile devices.","url_abs":"http://arxiv.org/abs/1712.01990v1","url_pdf":"http://arxiv.org/pdf/1712.01990v1.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":"a-scalable-deep-neural-network-architecture","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":"a-scalable-deep-neural-network-architecture","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":"classification","task_name":"General Classification"},{"task_slug":"indoor-localization","task_name":"Indoor Localization"},{"task_slug":"multi-label-classification-2","task_name":"MUlTI-LABEL-ClASSIFICATION"},{"task_slug":"multi-label-classification","task_name":"Multi-Label Classification"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}