Towards Predictive Fatigue Management: A Blockchain-Enabled IoT Framework for Driver Safety in Logistics
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Dec 27, 2024
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Abstract
Driver fatigue is a critical issue in the logistics industry, contributing significantly to accidents and operational inefficiencies. The system utilizes IoT devices, including physiological and vehicular sensors, to monitor real-time data, such as Heart Rate Variability (HRV), Galvanic Skin Response (GSR), and Acceleration Variance (AV). Blockchain integration ensures secure, immutable data storage and transparency, with smart contracts automating fatigue alerts and management actions. The research results show that HRV increased from 50 ms to 70 ms, reflecting better stress recovery, while AV decreased from 0.85 m/s² to 0.45 m/s², indicating more stable driving behavior. Fatigue alerts dropped by 60%, from 25 to 10 alerts per observation period, demonstrating the system’s effectiveness in early fatigue detection and prevention. The study concludes that the IoT-blockchain integration provides a robust, scalable solution for mitigating fatigue-related risks, enhancing driver safety, and fostering operational efficiency in the logistics sector.
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This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
How to Cite
Miftahol Arifin, Noor Qisthani, N., Titan Syifa, F., Ananda Raharja, P., Fanny, D., & Faizah, F. (2024). Towards Predictive Fatigue Management: A Blockchain-Enabled IoT Framework for Driver Safety in Logistics. Jurnal E-Komtek (Elektro-Komputer-Teknik), 8(2), 304-317. https://doi.org/10.37339/e-komtek.v8i2.2165
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[4] S. Liu, Y. Wang, Q. Yu, J. Zhan, H. Liu, and J. Liu, “A Driver Fatigue Detection Algorithm Based on Dynamic Tracking of Small Facial Targets Using YOLOv7,” IEICE Trans. Inf. Syst., vol. E106.D, no. 11, pp. 1881–1890, 2023, doi: 10.1587/transinf.2023EDP7093.
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[21] L. Minin, S. Benedetto, M. Pedrotti, A. Re, and F. Tesauri, “Measuring the effects of visual demand on lateral deviation: A comparison among driver’s performance indicators,” Appl. Ergon., vol. 43, no. 3, pp. 486–492, 2012, doi: https://doi.org/10.1016/j.apergo.2011.08.001.
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