INDUSTRIAL ENVIRONMENT MONITORING AND ALERT SYSTEM USING IOT

ABSTRACT: The Industrial Environment Monitoring and Alert System using IoT is designed to enhance workplace safety and efficiency by integrating gas sensors, heat sensors, and an alarm system. This system continuously monitors industrial environments for potential hazards such as gas leaks and abnormal temperature fluctuations. The gas sensor detects harmful gases (e.g., methane, carbon monoxide), while the heat sensor monitors temperature levels to prevent overheating and fire hazards.If any parameter exceeds predefined safety limits, the system triggers an alarm and can send real-time alerts to concerned authorities via IoT-based communication. This project leverages IoT technology to enable remote monitoring and data logging, improving real- time decision-making and preventive maintenance. The collected data can be analyzed to predict hazardous conditions, ensuring a safer and more efficient industrial environment. Overall, this smart industrial safety system minimizes risks, enhances automation, and optimizes industrial operations by providing a proactive safety mechanism. Industries involving hazardous gases and high-temperature machinery pose significant safety challenges. Conventional safety measures rely heavily on human supervision, which can be prone to errors and inefficiencies. The integration of Internet of Things (IoT) technology in industrial safety systems offers a real-time, automated, and scalable solution for preventing accidents and ensuring a secure working environment. Several research studies highlight the role of IoT- based industrial monitoring systems in enhancing workplace safety. Smart sensors, cloud computing, and data analytics are commonly used to detect environmental anomalies and prevent accidents. Prior studies emphasize gas detection technologies, temperature monitoring, and wireless alert mechanisms for early hazard identification. However, existing systems often lack real-time remote monitoring capabilities and efficient data processing, which this study aims to improve.