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Assessing the impact of coupling RTT and RSSI measurements in fingerprinting Wi-Fi indoor positioning González Díaz, Néstor Zola, Enrica Valeria Martín Escalona, Israel Universitat Politècnica de Catalunya. Departament d'Enginyeria Mecànica Universitat Politècnica de Catalunya. Departament d'Enginyeria Telemàtica Universitat Politècnica de Catalunya. ISG-MAK - Information Security Group - Mathematics Applied to Cryptography Àrees temàtiques de la UPC::Enginyeria de la telecomunicació::Telemàtica i xarxes d'ordinadors Wireless communication systems Wireless LANs Indoor localization Fingerprinting Machine learning RTT RSSI Accuracy Comunicació sense fil, Sistemes de Xarxes locals sense fil Wi-Fi The field of Indoor Positioning Systems (IPS) is rapidly expanding due to the increasing need for accurate indoor localization. This research delves into the fingerprinting technique, a commonly used method in IPS, and advocates for coupling the Received Signal Strength Indicator (RSSI) to the Round-Trip Time (RTT) to boost its effectiveness. The primary incentive for this integration is to alleviate the network burden caused by the Wi-Fi RTT method while maintaining the system’s precision. Our goal is two-fold: firstly, we aim to find the ideal combination of RTT and RSSI features that a specific machine learning algorithm requires to supply precise and prompt position estimations for real-time applications. Secondly, we aim to reduce the number of RTT features needed, as they demand the addition of location traffic, which may saturate the network when multiple stations try to locate themselves. To meet these goals, a variety of machine learning models and several combinations of the available metrics (RTT and RSSI) have been assessed. Initial findings indicate that this combined approach significantly diminishes network overhead and enhances the scalability and effectiveness of the fingerprinting method, paving the way for further exploration in indoor localization. Peer Reviewed Postprint (author's final draft) 2023 Conference lecture Gonzalez, N.; Zola, E.; Martin-Escalona, I. Assessing the impact of coupling RTT and RSSI measurements in fingerprinting Wi-Fi indoor positioning. A: International ACM Conference on Modeling, Analysis and Simulation of Wireless and Mobile Systems. "MSWiM '23: 26th ACM International Conference on Modeling, Analysis and Simulation of Wireless and Mobile Systems: Montreal Quebec, Canada: 30 october-3 november,2023: proceedings". New York: Association for Computing Machinery (ACM), 2023, p. 19-26. ISBN 979-8-4007-0366-9. DOI 10.1145/3616388.3617528. 979-8-4007-0366-9 https://hdl.handle.net/2117/404804 10.1145/3616388.3617528 eng https://dl.acm.org/doi/10.1145/3616388.3617528 http://creativecommons.org/licenses/by-nc-nd/4.0/ Open Access Attribution-NonCommercial-NoDerivatives 4.0 International 8 p. application/pdf Association for Computing Machinery (ACM)