Authors : Brijeshkumar U. Patel; Dr. Kaushika D. Patel

Volume/Issue : Volume 11 - 2026, Issue 8 - August

Google Scholar : https://tinyurl.com/5e7vkt7d

Scribd : https://tinyurl.com/2xmvb28e

DOI : https://doi.org/10.38124/ijisrt/26aug622

Note : A published paper may take 4-5 working days from the publication date to appear in PlumX Metrics, Semantic Scholar, and ResearchGate.

Abstract : The development of robust indoor positioning sys-tems requires high-quality fingerprint datasets that capture both Round Trip Time (RTT) and Received Signal Strength Indicator (RSSI) measurements. This paper presents the design and implementation of an Android-based data collection appli-cation that systematically captures RTT and RSSI values for large-scale indoor navigation systems. The application leverages the IEEE 802.11mc Fine Time Measurement (FTM) protocol through the Android Ranging API, enabling precise distance measurements alongside traditional RSSI fingerprinting. We describe the system architecture, data collection methodology, and application features designed to address the scalability challenges of fingerprint data collection. The application supports automated reference point traversal, multi-device calibration, and comprehensive metadata logging including LOS/NLOS condi-tions, device orientation, and temporal information. Performance evaluation demonstrates that the application achieves reliable RTT measurements with sub-meter accuracy in LOS conditions, while the integrated data export functionality facilitates seamless integration with existing fingerprinting algorithms and public benchmark datasets. The application addresses critical challenges in large-scale deployment, including device heterogeneity, tempo-ral dynamics, and data quality control.

Keywords : Android Application, Wi-Fi RTT, RSSI Finger-Printing, IEEE 802.11mc, Data Collection, Indoor Positioning, Mobile Sensing.

References :

1. X. Feng, K. A. Nguyen, and Z. Luo, “Robust indoor positioning with hybrid WiFi RTT-RSS signals,” Sensors, vol. 26, no. 1, p. 284, 2026.
2. L. Rana and J. G. Park, “An enhanced indoor positioning method based on RTT and RSS measurements under LOS/NLOS environment,” TechRxiv, 2024.
3. “IEEE Standard for Information Technology—Telecommunications and Information Exchange between Systems—Local and Metropoli-tan Area Networks—Specific Requirements—Part 11: Wireless LAN Medium Access Control (MAC) and Physical Layer (PHY) Specifi-cations—Amendment 8: IEEE 802.11mc,” IEEE Std 802.11mc-2016, 2016.
4. M. Bullmann, T. Fetzer, F. Ebner, M. Schmid, and A. Steinhauser, “Comparison of 802.11mc RTT and RSSI based indoor positioning,” in Proc. 2020 Int. Conf. Localization GNSS (ICL-GNSS), 2020.
5. Android Developers, “RangingManager API Reference,” Android Doc-umentation, 2024.
6. L. Rana and J. G. Park, “An enhanced indoor positioning method based on RTT and RSS measurements under LOS/NLOS environment,” Kyungpook National University, 2024.
7. X. Feng, K. A. Nguyen, and Z. Luo, “Conformal prediction for hybrid WiFi RTT-RSS indoor positioning,” IEEE Trans. Mobile Comput., 2025.
8. H. Lu and S.-H. Hwang, “Robot-assisted RSSI data collection for indoor fingerprint-based positioning,” ScienceDirect, 2025.
9. X. Feng, K. A. Nguyen, and Z. Luo, “WiFi RTT RSS dataset for indoor positioning,” Zenodo, 2024.
10. “Building RSSI-based indoor positioning fingerprint maps using Android-based coordination,” IEEE Xplore, 2024.
11. S. Zhu et al., “Large-scale indoor localization via outdoor crowdsourcing trajectories on ride-hailing platform,” Proc. ACM Interactive, Mobile, Wearable Ubiquitous Technol., 2025.
12. “WiFi RTT Checker App,” GitHub Repository, 2024.
13. “Open Mobile Network Toolkit (OMNT),” Fraunhofer FOKUS, 2024.
14. “UVIndoorLoc-RTT&RSSI: A new multi-scenario dataset for Wi-Fi fine time measurements indoor localization problems,” Zenodo, 2026.
15. “UJIIndoorLoc: A new multi-building and multi-floor database for WLAN fingerprint-based indoor localization problems,” Zenodo, 2025.
16. E. Zola, I. Martín-Escalona, and N. G. Diaz, “Coupling RTT and RSSI in fingerprinting Wi-Fi indoor positioning under NLoS conditions,” in Proc. Int. Conf. Modeling, Anal. Simulation Wireless Mobile Syst., 2025.