Leveraging RSSI and RTT for Accurate Distance Prediction in Bluetooth HC-05 with Multivariate Linear Regression Model
Received: 1 October 2025 | Revised: 17 October 2025, 5 November 2025, and 20 November 2025 | Accepted: 22 November 2025 | Online: 9 February 2026
Corresponding author: Farid Baskoro
Abstract
A model for the accurate estimation of the distance between Bluetooth devices using a Multivariate Linear Regression (MLR) approach that integrates Received Signal Strength Indicator (RSSI) and Round-Trip Time (RTT) data is presented in this study. Bluetooth technology, specifically the HC-05 module, is employed for wireless communication between devices, where RSSI and RTT serve as independent variables for distance prediction. The present study aims to address the limitations of using these methods separately, as RSSI is susceptible to environmental factors and signal interference, whereas RTT provides a more accurate measurement but often requires more complex calculations. By integrating both methods using an MLR model, a more robust and accurate distance estimate was achieved. The proposed model exhibited a Mean Squared Error (MSE) of 0.0173, indicating a very small average error in distance predictions, while the R-squared (R²) value of 0.9986 demonstrated that the model explained 99.86% of the variance in the actual distance data, highlighting its high accuracy. A Root Mean Squared Error (RMSE) of 0.1316 m, or approximately 13.16 cm, indicates that the model's average prediction error is around 13 cm. This approach significantly improves the reliability of Bluetooth-based localization systems and is highly beneficial for applications that require precise distance measurements.
Keywords:
bluetooth, RSSI, RTT, estimated distanceDownloads
References
I. F. Tabib, "A review of Bluetooth Technology," ResearchGate, 2023.
R. Shi, "The world of the Bluetooth," in Third International Conference on Electronics and Communication; Network and Computer Technology (ECNCT 2021), Harbin, China, Mar. 2022, vol. 12167, pp. 161–167. DOI: https://doi.org/10.1117/12.2628460
N. Rajeswari, H. M. Breesam, N. A. Dawod, C. Bharath, C. Vivek, and K. Pavanchand, "IOT-based Smart Surveillance Robotic Car using HC05," in 2024 International Conference on Augmented Reality, Intelligent Systems, and Industrial Automation (ARIIA), Manipal, India, Dec. 2024, pp. 1–6. DOI: https://doi.org/10.1109/ARIIA63345.2024.11051986
D. Suresh, P. V. Joshi, and P. Parandkar, "Mitigating Relay Attacks in Vehicle Access Systems Using BLE and UWB," Engineering, Technology & Applied Science Research, vol. 15, no. 5, pp. 26965–26970, Oct. 2025. DOI: https://doi.org/10.48084/etasr.12581
R. Ramirez, C.-Y. Huang, C.-A. Liao, P.-T. Lin, H.-W. Lin, and S.-H. Liang, "A Practice of BLE RSSI Measurement for Indoor Positioning," Sensors, vol. 21, no. 15, Jan. 2021, Art. no. 5181. DOI: https://doi.org/10.3390/s21155181
S. Barai, D. Biswas, and B. Sau, "Estimate distance measurement using NodeMCU ESP8266 based on RSSI technique," in 2017 IEEE Conference on Antenna Measurements & Applications (CAMA), Tsukuba, Japan, Dec. 2017, pp. 170–173. DOI: https://doi.org/10.1109/CAMA.2017.8273392
M. Omer and G. Y. Tian, "Indoor distance estimation for passive UHF RFID tag based on RSSI and RCS," Measurement, vol. 127, pp. 425–430, Oct. 2018. DOI: https://doi.org/10.1016/j.measurement.2018.05.116
G. Li, E. Geng, Z. Ye, Y. Xu, J. Lin, and Y. Pang, "Indoor Positioning Algorithm Based on the Improved RSSI Distance Model," Sensors, vol. 18, no. 9, Sept. 2018, Art. no. 2820. DOI: https://doi.org/10.3390/s18092820
G. Arslan, "Improving accuracy of round-trip time (RTT) in high accuracy distance measurements (HADM)," US12245182B2, Mar. 04, 2025.
M. A. Attar, N. Yadav, A. R. Singh, V. Jeyakumar, S. Gandham, and R. F. Spadaro, "Round trip time (RTT) measurement based upon sequence number," US10116531B2, Oct. 30, 2018.
A. Hashmi, "A Novel Drone-based Search and Rescue System using Bluetooth Low Energy Technology," Engineering, Technology & Applied Science Research, vol. 11, no. 2, pp. 7018–7022, Apr. 2021. DOI: https://doi.org/10.48084/etasr.4104
K. Sangrit, J. Karnjana, S. Laitrakun, K. Fukawa, S. Fugkeaw, and S. Keerativittayanun, "Distance Estimation Between Wireless Sensor Nodes Using RSSI and CSI with Bounded-Error Estimation and Theory of Evidence for a Landslide Monitoring System," in 2021 13th International Conference on Information Technology and Electrical Engineering (ICITEE), Chiang Mai, Thailand, Oct. 2021, pp. 40–45. DOI: https://doi.org/10.1109/ICITEE53064.2021.9611887
Seiya H. et al., "RSSI-Based Distance Estimation Enhanced by Interference Signals," IEICE Proceedings Series, vol. 74, no. A3L-D-3, pp. 101–104, Nov. 2020.
M. Slawski, E. Ben-David, and P. Li, "Two-Stage Approach to Multivariate Linear Regression with Sparsely Mismatched Data," Journal of Machine Learning Research, vol. 21, no. 204, pp. 1–42, 2020. DOI: https://doi.org/10.1214/18-EJS1498
C. G. Manriquez-Padilla, I. Cueva-Perez, A. Dominguez-Gonzalez, D. A. Elvira-Ortiz, A. Perez-Cruz, and J. J. Saucedo-Dorantes, "State of Charge Estimation Model Based on Genetic Algorithms and Multivariate Linear Regression with Applications in Electric Vehicles," Sensors, vol. 23, no. 6, Jan. 2023, Art. no. 2924. DOI: https://doi.org/10.3390/s23062924
D. ALabdeh, B. Omidvar, A. Karbassi, and A. Sarang, "Study of speciation and spatial variation of pollutants in Anzali Wetland (Iran) using linear regression, Kriging and multivariate analysis," Environmental Science and Pollution Research, vol. 27, no. 14, pp. 16827–16840, May 2020. DOI: https://doi.org/10.1007/s11356-020-08126-3
P. V. Mahesh, S. Meyyappan, and R. K. R. Alla, "A New Multivariate Linear Regression MPPT Algorithm for Solar PV System with Boost Converter," ECTI Transactions on Electrical Engineering, Electronics, and Communications, vol. 20, no. 2, pp. 269–281, June 2022. DOI: https://doi.org/10.37936/ecti-eec.2022202.246909
B. C. Haas, A. E. Goetz, A. Bahamonde, J. C. McWilliams, and M. S. Sigman, "Predicting relative efficiency of amide bond formation using multivariate linear regression," Proceedings of the National Academy of Sciences, vol. 119, no. 16, Apr. 2022, Art. no. e2118451119. DOI: https://doi.org/10.1073/pnas.2118451119
A. Pavlov and M. Holovchenko, "Modified Method of Constructing a Multivariate Linear Regression Given by a Redundant Description," Bulletin of National Technical University "KhPI". Series: System Analysis, Control and Information Technologies, vol. 2, no. 8, pp. 3–8, Dec. 2022. DOI: https://doi.org/10.20998/2079-0023.2022.02.01
H. A. Obeidat et al., "An Indoor Path Loss Prediction Model Using Wall Correction Factors for Wireless Local Area Network and 5G Indoor Networks," Radio Science, vol. 53, no. 4, pp. 544–564, Apr. 2018. DOI: https://doi.org/10.1002/2018RS006536
T. O. Hodson, "Root-mean-square error (RMSE) or mean absolute error (MAE): when to use them or not," Geoscientific Model Development, vol. 15, no. 14, pp. 5481–5487, July 2022. DOI: https://doi.org/10.5194/gmd-15-5481-2022
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Copyright (c) 2025 Farid Baskoro, Rifqi Firmansyah, Wahyu S. Putro, Widi Aribowo, Aristyawan P. Nurdiansyah
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