Development of an IoT-based control system for real-time heat stress management in the oil and gas sector

This study presents the design of a scalable IoT-based control system for mitigating heat stress in oil and gas workers through real-time monitoring and rule-based proactive decision-making. The system consists of an ESP32 microcontroller with environmental (DHT11), physiological (MAX30100) and motion (PIR) sensors. The data has been collected at ambient temperature and heart rate corresponding to the activity level of the worker. In contrast to traditional wearable solutions, the designed system is centrally coordinated, collects real-time data, conducts server-side filtering and apply logical algorithms to estimate core temperature, work intensity and heat stress index (HSI). This HSI assists in determining rest periods, hydration strategies, work-rest cycles and the use of cooling adjuncts through rule-based decision-making. This data is monitored by facility managers to avoid heat-related incidents through proactive measures. The outcomes are useful for organizations to maintain workers’ health status, achieve operational efficiency and meet regulatory requirements while working in extreme operating conditions. Keywords: Cloud-based monitoring; Heat stress management; Heat stress index; IoT; Oil and gas; Rule-based decision-making