Improving DBSCAN for Indoor Positioning Using Wi-Fi Radio Maps in Wearable and IoT Devices

Abstract

IoT devices and wearables may rely on Wi-Fi finger-printing to estimate the position indoors. The limited resources of these devices make it necessary to provide adequate methods to reduce the operational computational load without degrading the positioning error. Thus, the aim of this article is to improve the positioning error and reduce the dimensionality of the radio map by using an enhanced DBSCAN. Moreover, we provide an additional analysis of combining DBSCAN + PCA analysis for further dimensionality reduction. Thereby, we implement a postprocessing method based on the correlation coefficient to join "noisy" samples to the formed clusters with Density-based Spatial Clustering of Applications with Noise (DBSCAN). As a result, the positioning error was reduced by 10% with respect to the plain DBSCAN, and the radio map dimensionality was reduced in both dimensions, samples and Access Points (APs).