{"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/autonomous-wifi-fingerprinting-for-indoor","title":"Autonomous WiFi Fingerprinting for Indoor Localization","arxiv_id":"1911.11825","date":"2019-11-26","proceeding":null,"authors":[],"abstract":"WiFi-based indoor localization has received extensive attentions from both\nacademia and industry. However, the overhead of constructing and maintaining\nthe WiFi fingerprint map remains a bottleneck for the wide-deployment of\nWiFi-based indoor localization systems. Recently, robots are adopted as the\nprofessional surveyor to fingerprint the environment autonomously. But the time\nand energy cost still limit the coverage of the robot surveyor, thus reduce its\nscalability. To fill this need, we design an AutonomousWiFi Fingerprinting\nsystem, called AuF, which autonomously constructs the fingerprint database with\ntime and energy efficiency. AuF first conduct an automatic initialization\nprocess in the target indoor environment, then constructs the WiFi fingerprint\ndatabase of in two steps: (i) surveying the site without sojourn, (ii)\nrecovering unreliable signals in the database with two methods. We have\nimplemented and evaluated AuF using a Pioneer 3-DX robot, on two sites of our\n$70$$\\times$$90$m$^2$ Department building with different structures and\ndeployments of access points (APs). The results show AuF finishes the\nfingerprint database construction in 43/51 minutes, and consumes 60/82 Wh on\nthe two floors respectively, which is a 64%/71% and 61%/64% reduction when\ncompared to traditional site survey methods, without degrading the localization\naccuracy.","url_abs":"http://arxiv.org/abs/1911.11825v1","url_pdf":"http://arxiv.org/pdf/1911.11825v1.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":"autonomous-wifi-fingerprinting-for-indoor","repo_url":"https://github.com/sldai/WiFi_localization","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[{"task_slug":"indoor-localization","task_name":"Indoor Localization"}],"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}