WAIPO: A Fusion-Based Collaborative Indoor Localization System on Smartphones

Indoor localization based on smartphone can enhance user's experiences in indoor environments. Although some innovative solutions have been proposed in the past two decades, how to accurately and efficiently localize users in indoor environments is still a challenging problem. Traditional indoor positioning systems based on Wi-Fi fingerprints or dead reckoning suffer from the variation of Wi-Fi signals and the drift of dead reckoning problems, respectively. Crowdsourcing and ambient sensing stimulate new ways to improve existing localization systems' accuracy. Using human social factors to calibrate the accuracy of localization is practical and awarding. In this paper, we propose WAIPO, a collaborative indoor localization system with the fusion of Wi-Fi and magnetic fingerprints, image-matching, and people co-occurrence. Specifically, we could obtain the most likely top-n locations based on Wi-Fi fingerprints. We utilize the statistics of users' historical locations known by image-matching, for which we propose a photo-room matching algorithm, to reduce estimating areas. In order to further improve the accuracy of localization, we propose a co-occurrence and non-co-occurrence detection algorithm to detect users' spatial-temporal co-occurrence and determine users' locations with magnetic calibration. We have fully implemented WAIPO on the Android platform and perform testbed experiments. The experimental results demonstrate that WAIPO achieves an accuracy of 87.3% on average, which outperforms the state-of-the-art indoor localization systems.