Utilizing Internet of Things (IoT) technology for monitoring air quality through the detection of particulate matter, with data processed and stored on cloud servers using the Online Think speak platform

Electronics and Communication Engineering

This paper presents a platform that uses Internet of Things (IoT) and cloud computing technologies to monitor both interior and outdoor pollution, enabling environmental quality assessment at any time and in any location. The system captures data at regular intervals from multiple locations inside and outside a room, providing valuable information for characterising the current conditions. Monitoring and reporting environmental factors such as gas levels, particulate presence, temperature, and humidity, the Pollution Monitoring System exemplifies the efficient application of technology. However, communication between sensing hardware devices and MQTT software protocols for online monitoring on any cloud platform presents a number of obstacles. To address these issues, IoT-enabled hardware devices are programmed to measure various air quality metrics and transmit the data to Thinkspeak analytics and the Cloud Platform for accounting and graphical representation. The system employs the MQTT protocol to establish communication between hardware devices and the cloud server. The primary objective of this research is to develop an Internet of Things-based air quality monitoring system that is both real-time and cost-effective. The system employs a dust level or particulate matter sensor for monitoring dust levels, while DHT11 sensors measure temperature and humidity. In addition, an ATmega328 controller with a NodeMCU ESP8266 development board equipped with a WiFi module is used to transmit readings to a ThingSpeak web channel platform, enabling immediate and real-time air quality display. The concentration level is graphically monitored through channels on ThingSpeak to facilitate remote communication. A threshold value has been established, so when pollutant levels reach high concentrations, the graphical curve rises to represent the increased particulate concentration level. Using NodeMCU and ThingSpeak, the study effectively designs a low-cost air quality monitoring system.

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