Transfer learning for WiFi-based indoor localization

Abstract

The WiFi-based indoor localization problem (WILP) aims to detect the location of a client device given the signals received from various access points. WILP is a complex and very important task for many AI and ubiquitous computing applications. A major approach to solving this task is through machine learning, where upto-date labeled training data are required in a large scale indoor environment. In this paper, we identify WILP as a transfer learning problem, because the WiFi data are highly dependent on contextual changes. We show that WILP can be modeled as a transfer learning problem for regression modeling, where we identify several important cases of knowledge transfer that range from transferring the localization models over time, across space and across client devices. We also share our working experience in WILP and transfer learning research in a realistic problem solving setting, and discuss a data set we have made public for advancing this research.