Published Paper Details:

Industrial environments require continuous mon itoring of multiple environmental parameters to ensure the safety of personnel and infrastructure. Conventional monitoring systems often present raw sensor readings without meaningful interpretation, limiting their effectiveness in real-world safety applications. This paper presents HazardSense, a centralized Internet of Things (IoT)-based environmental safety monitor ing system designed to interpret multi-sensor data and assess environmental risk conditions. The system integrates gas, tem perature, humidity, flame, and light sensors using an ESP32 microcontroller and processes the collected data to classify safety conditions based on predefined thresholds. Instead of focusing solely on individual sensor outputs, HazardSense correlates sensor readings to determine severity levels and provide action able safety insights. Experimental evaluation under controlled conditions demonstrates the system's ability to identify unsafe environmental states and support early warning mechanisms. The proposed approach offers a cost-effective, scalable, and practical solution for enhancing situational awareness in industrial and indoor environments.