Development and Performance Evaluation of an IoT-Based Real-Time Heat Index Monitoring and Alert System for Military Training: A Case Study of the Signal Battalion, Directorate of Communications and Information Technology, Royal Thai Armed Forces Headquarters
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Abstract
Heat-related illness poses a significant risk to personnel safety and training effectiveness in military environments, while existing monitoring practices often lack real-time heat-risk assessment and alert capabilities. This study aimed to 1) develop an Internet of Things (IoT)-based real-time heat index monitoring and alert system for military training, 2) evaluate the technical performance of the developed system in terms of sensor validation, heat-risk rule-based classification verification, data transmission reliability, and alert latency, and 3) assess user satisfaction. This study employed a research and development (R&D) approach based on the System Development Life Cycle (SDLC). The developed system integrated an SHT31 temperature and humidity sensor, an ESP32 microcontroller, Firebase Realtime Database, a web-based dashboard, and a LINE Official Account. The study included all 40 personnel participating in the 2026 military discipline training program at the Directorate of Communications and Information Technology, Royal Thai Armed Forces Headquarters, using a census approach. Technical performance was evaluated through 50 predefined heat-risk classification test cases, 100 data transmission trials, and 10 alert latency measurements. The results demonstrated that the developed system achieved 100.00% correctness in heat-risk rule-based classification verification under controlled testing conditions and a 100.00% data transmission success rate, with an average alert latency of 2.49 seconds. Overall user satisfaction was rated at the highest level (x̄ = 4.71, S.D. = 0.38). The system effectively integrates real-time heat index computation, military Heat Flag classification, cloud-based data synchronization, and instant LINE notifications within a single platform, thereby enhancing heat-risk monitoring and supporting timely safety decision-making during military training. These findings demonstrate the practical applicability of IoT-based real-time monitoring technologies for improving occupational safety in military training and other outdoor operational environments and provide a valuable framework for the future research and development of intelligent safety monitoring systems in high-risk settings
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