On TinyML WiFi Fingerprinting-Based Indoor Localization: Comparing RSSI vs. CSI Utilization

Abstract

As context-aware location-based services (LBS) become increasingly important in many Internet of Things (IoT) verticals, such as logistics or industry 4.0, indoor localization is now an essential feature to be integrated in these solutions. For this purpose, fingerprinting-based solutions arise as a feasible solution, especially when integrating artificial intelligence on the edge, supported by computational and memory-restricted embedded devices, as it does not depend on a cloud-based deployment. In this work, we integrate this new paradigm, known as TinyML, and compare the implementation of a machine learning (ML) model when using only WiFi Received Signal Strength Indicator (RSSI) or WiFi Channel State Information (CSI) data. We tested two different scenarios, a single sample or time series, with different configurations of the trained neural network. Our results show that a CSI data ML model always outperforms an equivalent RSSI approach, with a massive difference in performance for the time-series case.