Selecting Critical WiFi APs for Indoor Localization Based on a Theoretical Error Analysis

Abstract

The radio map built during the offline phase of any WiFi fingerprint-based localization system usually scales with the number of WiFi access points (APs) that can be detected at a single location by any mobile device, but this number in practice can be as large as 100, only a few of which essentially contribute to localization. Simply involving all the APs in location fingerprints and thus in the radio map not only wastes excessive storage but also leads to a large computational overhead. In this paper, a theoretical analysis of WiFi-based location determination is conducted to investigate the resulting localization errors and reveals that some critical APs contribute significantly more to localization than the others, implying that it is unnecessary to include every AP in localization. Consequently, a heuristic AP selection algorithm based on the error analysis is proposed to efficiently select a subset of APs for use in localization. Finally, extensive experiments are carried out by using both the UJI dataset available online and the dataset collected in our laboratory, and it is shown that the proposed algorithm not only significantly reduces the redundancy of APs in WiFi fingerprint-based localization but also substantially improves the localization accuracy of the $k$ nearest neighbor (KNN) method.