Transferring Localization Models over Time

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Indoor localization in multi-floor environments with reduced effort (2010, background): “[17] and [18] addressed the environmental changes with time and devices in WiFi localization with minimal effort; however, both of them required at least some labeled data in the new environments (i.”
Semi-supervised deep extreme learning machine for Wi-Fi based localization (2015, methodology): “The mainstream approach of Wi-Fi based indoor localization, fingerprint based method [2,3], consists of two phases: off-line training phase and online locating phase.”
CityTransfer (2018, methodology): “For example, Zheng et al. [41] study the problem of transfer learning using a semi-supervised HMM for adaptive localization in an indoor environment.”
A Survey on Transfer Learning (2010, background): “In [75], [76], [77], [78], [79], transfer learning techniques are proposed to extract knowledge from WiFi localization models across time periods, space, and mobile devices, to benefit WiFi 1354 IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING, VOL. 22, NO. 10, OCTOBER 2010 7.”
Feature-based transfer learning with real-world applications (2010, background): “For example, transfer learning techniques were proposed to extract knowledge from WiFi localization models across time periods [217, 136, 230], space [138, 194] and mobile devices [229], to benefit WiFi localization tasks in other settings.”