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The core of fingerprinting is based on the uniqueness of the RF signature in a given location over time. In the offline phase, the fingerprints -the set of RSSI values from different anchors-are collected at given locations generating a radio map. In the online phase, a matching algorithm retrieves the most similar fingerprints from the radio map and computes the position estimate for every operational fingerprint. However, computing the similarities to all the samples in the radio map may be inefficient and not scale in those cases where the radio map is large. Previous attempts to alleviate the computational load rely on the segmentation of the radio map through smart clustering in the offline stage, and a two-step estimation process in the online stage. However, most of the clustering models applied are generic without any consideration about signal propagation and relevant fingerprints are often filtered, resulting in a higher positioning error. This paper introduces Strongest AP Set (SAS), a clustering model conceived for RSSI-based fingerprinting. The results show that SAS is not only able to reduce the computational cost, but also to provide better accuracy than the full model without clustering.

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