Prototyping LoRaWAN-Based Mobile Air Quality Monitoring System for Public Health and Safety

In this paper, we present the design, prototyping, and working of a cost-effective, energy-efficient, and scalable air quality monitoring system (AQMS), enabled by a Low-power, long-Range Wide-Area Network (LoRaWAN), an Internet of Things (IoT) technology designed to provide connectivity for massive machine-type communication applications. The growing threat of air pollution necessitates outdoor and mobile environmental monitoring systems to provide real-time, location-specific data, which is unfortunately not possible using fixed monitoring devices. For our AQMS, we have developed two custom-built sensor nodes. The first node is equipped with a Nucleo-WL55JC1 microcontroller and sensors to measure temperature, humidity, and carbon dioxide (CO2), while the other node is equipped with an Arduino MKR WAN 1310 controller with sensors to measure carbon monoxide (CO), ammonia (NH3), and particulate matter (PM2.5 and PM10). These sensor nodes connect to a WisGate Edge LoRaWAN gateway, which aggregates and forwards the sensor data to The Things Network (TTN) for processing and cloud storage. The final visualization is handled via the Ubidots IoT platform, allowing for real-time visualization of environmental data. Besides environmental data, we were able to acquire a received signal strength indicator, signal-to-noise ratio, as well as a frame counter, which shows the number of packets received by the gateway. We performed laboratory testing, which confirmed reliable communication, with a packet delivery rate of 98% and a minimal average latency of 2.5 s. Both nodes operated efficiently on battery power, with the Nucleo-WL55JC1 consuming an average of 20 mA in active mode, while the Arduino MKR WAN 1310 operated at 15 mA. These values ensured extended operation for remote deployment. The system’s low power consumption and modular architecture make it viable for smart city applications and large-scale deployments in resource-constrained areas.