Online RSSI selection strategy for indoor positioning in low-effort training scenarios

Abstract

Indoor positioning has been extensively studied for at least the past twenty years. In the list of the most common solutions, those based on the Received Strength Signal Indicator (RSSI) have gained importance due to the simplicity of RSSI as well as the fact that it is available in several wireless sensor networks. In this work, we propose SeALS (Selection Strategy of Access Points with Least Squares Estimation), a new RSSI-based indoor positioning system using Bluetooth Low-Energy (BLE) access points, whose accuracy is improved by a new selection strategy of collected RSSI combined with the Ordinary Least Squares (OLS) estimation method. The main advantage of the proposed solution is the fact that it requires less time in the training phase allied with better system accuracy if compared to traditional methods. The proposed system is validated in a large-scale, real-world scenario, and the obtained results for the positioning error are reduced by up to 13% concerning the pure OLS method, and by up to 30% concerning the widely deployed K-Nearest Neighbors technique.