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Abstract
This study proposes and develops an Internet of Things (IoT) system based on a Cyber-Physical System (CPS) approach for real-time monitoring of temperature, humidity, and location in refrigerated container transportation, supporting logistics training and applied research in cold chain management for temperature-sensitive goods. The system is designed as a low-cost solution suitable for practical training models and applied research in technical education. The data acquisition device is built around an ESP32 microcontroller and integrates a temperature-humidity probe based on an SHT40 sensing element and a NEO-6M GPS module. Data are transmitted to a cloud server via a 4G LTE network using an ML307R-DL module or via Wi-Fi, with a default update interval of 60 seconds. A 1:5-scale refrigerated container model was fabricated with polyurethane (PU) insulation and achieved a minimum temperature of approximately 6°C, which is appropriate for small-scale system verification. Experimental results indicate stable system operation, with data transmission latency of 200-300 ms, packet loss of less than 1%, and battery life of approximately 8 hours under high-load continuous operation with a 6-second data-transmission interval. The study contributes to the development of a visual cold chain monitoring model, emphasizing verification of the IoT-CPS architecture and supporting Problem-Based Learning (PBL) for Logistics 4.0-oriented education.
Keywords: Internet of Things (IoT), refrigerated container, cold chain supply chain, route monitoring, Cyber-Physical System (CPS), Logistics 4.0.