Abstract
Wi-Fi hot spots have quickly increased in recent years. Accordingly, discovering user positions by using Wi-Fi fingerprints has attracted much research attention. The Wi-Fi fingerprints are the sets of Wi-Fi scanning results recorded in mobile devices. However, the issue of data uncertainty is not considered in the proposed Wi-Fi positioning systems. In this paper, we propose a framework to find user trajectories in their daily life from the Wi-Fi fingerprints recorded in their smartphones. In this framework, we first discover places with the proposed Wi-Fi distance metric. Second, we propose two similarity functions to recognize the places and show the probabilities of the places where the user stayed in by our proposed uncertain data models. Final, an algorithm on probabilistic sequential pattern mining is used for finding user trajectories. A series of experiments are performed to evaluate each step of the framework. The experiment results reveal that the steps in our framework are all with high accuracy.