As the foundation of location-based services, accurate localization has attracted considerable attention. A typical wi-fi localization system employs a fingerprint-based method, which constructs a fingerprint database and returns user's location based on similar fingerprints. Existing systems cannot accurately locate users in a metropolitan-scale because of the requirement of large fingerprint data sets, complicated deployment, and the inefficient search algorithm. To address these problems, we develop a localization system called POLARIS. By the contribution of users, we construct a large fingerprint database. We introduce an effective localization model based on novel similarity measures of fingerprints. For fast localization, we devise an efficient algorithm for matching similar fingerprints, and develop a cluster-based representative fingerprint selection method to improve the performance. We conduct extensive experiments on real data sets, and the experimental results show that our method is accurate and efficient, significantly outperforming state-of-the-art methods.
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