Through the advent of industrialisation, the amount of air pollution has notably increased throughout recent history due to increasing personal vehicle use (car dependency), increasing number of factories and increasing amount of construction projects to name a few.
This work involves an IoT air quality monitoring system with consumer-grade electronic parts that are available on the international and local market. This would allow a person with some technical knowledge and skill to replicate such a system and not only evaluate the surrounding air quality, but also build a dataset, all at a low cost. The Internet of Things (IoT) is a technology that describes a network that connects objects in the physical world to the Internet.
Furthermore, this work also explores the current state of air quality monitoring, its current challenges, and what is being done to address them. We will also observe the effects of these challenges on this work.
The chosen approach was to connect several sensors to a small programmable computer, better known as a microcontroller, to measure the quality of the surrounding air. The air quality data collected from a remote location (by the sensing microcontrollers) is transmitted via their LoRa module wirelessly. The wireless data is then received by the master microcontroller via its ownLoRa module. It is physically connected to a PC, which allows the master microcontroller to write the data to the PC’s storage medium and hence, build the dataset.
LoRa is a long-range wireless communications protocol designed to transmit small amounts of data at very low power levels.
Sensors were selected to measure the following air quality characteristics:
The system was designed with the objective of it being scalable and low maintenance whilst providing accurate air quality data. Hence, parts were selected primarily based on cost, power consumption and sensor accuracy. Furthermore, the transmission technology was also selected based on power consumption and range. The sensors were calibrated to ensure accurate readings.
The system developed performed a month’s worth of outdoor air quality data collection. The system was tested on power consumption and accuracy with good results which were then compared to other existing solutions from other research papers. Data analysis was then performed on the collected data and the different correlations and relationships between the air quality features were determined. The results were then compared with theory.
For future work, the system was analysed to determine areas in which it can be improved. Longer term data collection was also considered to contribute towards data to be used to better calibrate the sensor readings through software-based calibration methods, particularly in machine learning.
Student: Galin Petkov Gluhov
Course: B.Sc. (Hons.) Computer Engineering
Supervisor: Dr Trevor Spiteri