Mining user trajectories from smartphone data considering data uncertainty

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. 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 from the Wi-Fi fingerprints recorded in the smartphones. In this framework, we first discover meaningful 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 a user stayed in by the proposed uncertain data models. Finally, 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 each step of our framework is with high accuracy.