Revisiting the Analysis of Hyperparameters in k-NN for Wi-Fi and BLE Fingerprinting: Current Status and General Results
Cristina Rodriguez-Martinez, Joaquín Torres-Sospedra
Abstract
Wi-Fi Fingerprinting is a very popular technique in the field of indoor positioning, since the release of Microsoft RADAR system back in 2000. Since that milestone, the vast majority of studies and improvements in this field keep using the same base algorithm, an adaptation of the k-NN algorithm to treat geospatial data (e.g., x/y or lat/lon). One of the most relevant drawbacks of k-NN algorithm resides in its initial design, focused on resolving general classification problems. Wi-Fi fingerprinting technique is based on the measurement of the signal strength emitted by close and available Wi-Fi networks. However, the nature of signal propagation is not linear, and it is impacted by the fixed and dynamic obstacles present in the environment. This work consists in the study of k-NN algorithm parameters, k value, distance metric and data representation, to improve the efficiency of this prediction model. The evaluation will be conducted over several different heterogeneous databases and propose a model to automatically set the value of k.