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All Journal Journal of Embedded Systems, Security and Intelligent Systems
Dzaki Putra Prakosa
Politeknik Keselamatan Transportasi Jalan
Computer Science & IT

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Real-Time Intelligent IoT-Based Drum Brake Wear Monitoring System Dzaki Putra Prakosa; Setya Wijayanta
Journal of Embedded Systems, Security and Intelligent Systems Vol 7 No 2 (2026): June 2026
Publisher : Program Studi Teknik Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59562/jessi.v7i2.2609

Abstract

Purpose – This study aims to develop and evaluate a real-time IoT-based intelligent monitoring system for drum brake lining wear to overcome the limitations of conventional manual inspection, which is periodic, subjective, and prone to delayed detection of critical wear conditions. Design/methods/approach – The research adopts a Research and Development (R&D) methodology consisting of design, prototyping, and laboratory testing. The system integrates an ESP32 microcontroller with a VL53L0X time-of-flight sensor to measure brake lining thickness in real time. A rule-based classification algorithm is implemented to categorize brake conditions into SAFE, WARNING, and DANGER states. Experimental evaluation was conducted through 15 trials across a thickness range of 1.0–10.0 mm. Performance metrics include accuracy, mean absolute error (MAE), root mean square error (RMSE), and response time. Findings - The system achieved an average measurement accuracy of 96.0%, MAE of 0.13 mm, and RMSE of 0.15 mm. All samples were correctly classified, resulting in 100% classification accuracy across the three condition states. The system also recorded a mean response time of 1.27 seconds, indicating fast and reliable real-time performance under controlled conditions. Research implications/limitations – The system is feasible for low-cost brake wear monitoring, but validation is limited to laboratory conditions with a small dataset, and real-world factors were not examined. Originality/value – This study presents an integrated IoT-based drum brake monitoring framework combining ToF sensing, embedded rule-based intelligence, and mobile notification in a single low-cost system. It specifically addresses drum brake applications, which remain underexplored compared to disc brake monitoring systems, offering a practical solution for resource-constrained environments.
Real-Time Ambulance Detection System at Traffic Intersections Using Raspberry Pi and YOLOv5 Muhammad Isro’ Risqi; Raka Pratindy; Dzaki Putra Prakosa
Journal of Embedded Systems, Security and Intelligent Systems Vol 7 No 2 (2026): June 2026
Publisher : Program Studi Teknik Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59562/jessi.v7i2.2635

Abstract

Purpose – Delays experienced by ambulances at signalized intersections remain a critical issue in emergency transportation, particularly in dense urban traffic conditions. This study aims to develop and evaluate a low-cost real-time ambulance detection system using Raspberry Pi 4B and YOLOv5 to support intelligent transportation monitoring and emergency vehicle prioritization. Design/methods/approach – This study employed an experimental research design by integrating CCTV cameras, Raspberry Pi 4B, YOLOv5s object detection, ONNX Runtime INT8 optimization, and Telegram Bot API notification. The model was trained using 3,250 annotated ambulance images divided into training, validation, and testing subsets. System performance was evaluated under five operational scenarios: daytime, nighttime, heavy traffic, long-distance detection, and low-lighting conditions. Findings – The proposed YOLOv5s model achieved precision of 95.4%, recall of 93.8%, mAP@0.5 of 96.1%, and sustained throughput of 22 FPS on Raspberry Pi 4B. The Telegram notification subsystem achieved a transmission success rate of 98.7% with an average delay of 1.8 seconds. However, detection performance decreased under low-lighting conditions, with a true positive rate of 78.5% and false positive rate of 11.2%. Research implications/limitations – The system demonstrates the feasibility of deploying embedded computer vision for cost-effective ambulance detection, although nighttime reliability and traffic signal integration require further improvement. Originality/value – This study contributes an ONNX INT8-optimized YOLOv5s implementation on Raspberry Pi 4B with multi-condition evaluation and real-time Telegram notification for ambulance detection at traffic intersections.
Co-Authors Muhammad Isro’ Risqi Raka Pratindy Setya Wijayanta