Evaluation of RSSI-Based Distance Estimation with ESP32 BLE Modules for Indoor Asset Tracking

Authors

  • Omar Al-Maktary Department of Electrical Engineering University of Lampung
  • Misfa Susanto [Scopus ID : 55820056300, FORTEI Member] Universitas Lampung, Department of Electrical Engineering, Bandar Lampung. https://orcid.org/0000-0002-4012-6244
  • Mardiana Mardiana Department of Electrical Engineering University of Lampung

DOI:

https://doi.org/10.26418/elkha.v17i2.97739

Keywords:

Bluetooth Low Energy, RSSI-based distance estimation, asset tracking, Real Time Location System, indoor positioning

Abstract

Bluetooth Low Energy (BLE) is a technology used for asset tracking, offering low power consumption and compatibility with embedded systems such as the ESP32. This paper evaluates the accuracy and reliability of Received Signal Strength Indicator based distance estimation using ESP32 BLE modules in three environmental conditions: clear line-of-sight, wall obstruction, and mobile tracking. It presents an empirical analysis of ESP32-specific RSSI limitations across these scenarios. The log-distance path loss model was employed, using a reference RSSI of -47 dBm at 1 meter and a path loss exponent of 2. Experiments were conducted with a BLE tag device (Asset_Tag_01) broadcasting BLE signals, while an ESP32 reader device collected RSSI data via Arduino IDE. Results indicate reliable estimation within 4 meters with under 25% error in line-of-sight conditions. However, beyond 5 meters, particularly in obstructed environments, RSSI values fluctuated significantly, causing distance overestimation. Wall obstructions resulted in an immediate 6 dBm signal degradation at just 1 meter. Packet loss increased from 0% at short distances to 50% at 8.5 meters. In mobile tracking, signal strength showed sudden jumps, complicating movement detection. These findings highlight that RSSI alone is not reliable for precise tracking. To improve accuracy, particularly in real-world settings like healthcare or industrial environments, further studies should explore advanced methods like Kalman filtering combining data from multiple sensors.

Author Biographies

Omar Al-Maktary, Department of Electrical Engineering University of Lampung

Omar Al-Maktary is currently a master degree student in Electrical Engineering Master Degree Program, Department of Electrical Engineering, University of Lampung, Indonesia.

Misfa Susanto, [Scopus ID : 55820056300, FORTEI Member] Universitas Lampung, Department of Electrical Engineering, Bandar Lampung.

Universitas Lampung, Department of Electrical Engineering, Bandar Lampung, Indonesia h-index: Scopus = 9, GS = 10

Mardiana Mardiana, Department of Electrical Engineering University of Lampung

Mardiana is currently a lecturer in Informatics, Department of Electrical Engineering, University of Lampung, Indonesia.

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2025-10-20

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Vol. 17 No.2 October 2025

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Vol. 17 No.2 October 2025

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