Published Paper Details:

In industrial settings, gas leaks, equipment overheating, and unlawful access into prohibited zones may cause serious accidents, equipment damage, and human lifethreatening situations. Manual monitoring of these parameters slows response time. This work presents an IOT-based Industrial Anomaly Detection and Alert System for gas leakage detection and restricted area incursion monitoring to safeguard workers and equipment. It employs IR and gas sensors. To improve safety, Machine Learning can identify dangers and categorize alerts. Microcontroller-based ML concept checks sensor data that pass the threshold range and sends an SMS to the authorized individual.Workers and equipment are protected by this system's swift response to risks. Cloud storage secures real-time sensor data from Arduino devices in industrial IoT safety solutions. It enables machine learning algorithms to easily assess and forecast gas leaks and overheating using three parameter measurement levels. Data centralization lets organizations monitor worker and equipment safety from anywhere.Integrating GPS tracking to monitor restricted areas in real time improves worker safety.