Enhancing Wi-Fi fingerprinting for indoor positioning using human-centric collaborative feedback

In this article, a robust and efficient model is devised for integrating human-centric collaborative feedback within a baseline Wi-Fi fingerprinting-based indoor positioning system. Experiments show that the baseline system performance (i.e., positioning error and precision) 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.