Smart Safety Helmet with LoRaWAN and Cloud-based Visualization for Real-time Gas Detection and Location Tracking Monitoring

Abstract

This project presents the design, development, and testing of a Smart Safety Helmet that integrates real-time gas detection and GPS-based location tracking using LoRaWAN technology for long-range, low-power communication. The system is aimed at enhancing occupational safety in hazardous environments such as construction sites, chemical facilities, and mining operations where exposure to combustible gases poses a significant risk. The device is built around the SAMD21 microcontroller, integrating an MQ-5 gas sensor to detect the concentration of liquefied petroleum gas (LPG) in the surrounding air. A GPS module (Air530) provides geolocation data, while a Grove LoRa-E5 module enables wireless transmission of sensor data to The Things Network (TTN), a LoRaWAN-based infrastructure. The data is decoded using a custom uplink payload formatter and visualized on the Datacake IoT dashboard, which provides real-time LPG concentration readings and live location tracking via an embedded map interface. The methodology involved iterative hardware prototyping, Arduino-based firmware development, payload encoding/decoding, and dashboard configuration. Field testing confirmed that the system could reliably measure LPG levels under typical ambient conditions and transmit this data alongside GPS coordinates at 30-second intervals. The results demonstrate that the helmet can function autonomously without direct USB power, making it suitable for mobile use. Key contributions include high-accuracy gas monitoring, reliable geolocation data, and end-to-end cloud-based visualization. The project highlights the viability of integrating low-power embedded systems with IoT infrastructure for improving worker safety. Limitations related to GPS availability and LoRa signal strength in obstructed environments were also observed, offering opportunities for future enhancement.