Wi-Fi-Based Indoor Positioning Using Human-Centric Collaborative Feedback

In recent years, "folksonomy"-like systems such as Wikipedia and Delicious Social Bookmarking have achieved huge successes. User collaboration is the defining characteristic of such systems. For indoor positioning mechanisms, we argue that it is also possible to incorporate collaboration in order to improve system performance, especially for fingerprinting based approaches. In this paper, we propose a robust and efficient model for integrating human-centric collaborative feedback within a baseline Wi-Fi fingerprinting-based indoor positioning system. Experimental results show that the baseline system performance (i.e., positioning error) is improved by collecting both positive and negative feedback from users. Moreover, the feedback model is robust with respect to malicious feedback, quickly self-correcting based on subsequent helpful feedback from users.