Fine-Grained Location Extraction and Prediction with Little Known Data

Location information has become a key component of many applications in mobile and pervasive computing, and the ability to accurately predict the mobility of clients allows these applications to provide better service. However, existing location predictors rely heavily on a significant amount of empirical knowledge to function well. In this paper, we develop a novel framework to predict unknown locations when only little location information is available. Specifically, we first extract WiFi locations from WiFi scan results, then a mobility model is built based on the resulted WiFi location graph with connectivity information, finally, we make location predictions with little known location data with Gibbs sampling over the mobility model. Using a data set containing 31 fairly complete WiFi traces collected over three months as ground truth, we compare our proposed approach with other existing state-of-the-art location predictors. The experimental results show that our framework can achieve 83% location prediction accuracy with only three location samples each day, 15% better than Markov and Bayesian predictors which heavily rely on empirical knowledge.