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A Comparative Review of Communication Technologies for Asset Tracking in Healthcare Facilities Al-Maktary, Omar; Susanto, Misfa
Applied Engineering, Innovation, and Technology Vol. 2 No. 1 (2025)
Publisher : MSD Institute

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62777/aeit.v2i1.59

Abstract

Efficient asset tracking in healthcare facilities is essential to reduce delays, prevent equipment loss, and optimize operational workflows. This study systematically reviews four widely used technologies: Radio Frequency Identification (RFID), Near Field Communication (NFC), Global Positioning System (GPS), and Bluetooth Low Energy (BLE), based on six criteria: cost, accuracy, range, energy efficiency, ease of deployment, and scalability. RFID and NFC offer high accuracy in short-range use cases, but RFID requires substantial infrastructure, while NFC is limited by its manual operation. GPS is highly effective for outdoor tracking, though it struggles indoors. BLE provides a strong balance across all criteria and supports long battery life, making it suitable for large-scale indoor tracking. The review incorporates real-world case studies and proposes hybrid IoT-based systems that combine these technologies to achieve comprehensive coverage. Future research should focus on seamless indoor-outdoor handoff, energy-efficient synchronization, and the use of machine learning for signal optimization.
Evaluation of RSSI-Based Distance Estimation with ESP32 BLE Modules for Indoor Asset Tracking Al-Maktary, Omar; Susanto, Misfa; Mardiana, Mardiana
ELKHA : Jurnal Teknik Elektro Vol. 17 No.2 October 2025
Publisher : Faculty of Engineering, Universitas Tanjungpura

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26418/elkha.v17i2.97739

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.