Intelligent Air Quality Detection Device Based on Edge Computing

Abstract

With the rapid advancement of industrialization and urbanization, the adverse effects of air pollution on human health and environmental protection have become increasingly significant. This study developed an air quality monitoring device equipped with various air detection sensors and integrated with a Wi-Fi sensor for data collection and cloud upload. A multilayer long short-term memory (LSTM) model was used to analyze the data, and strategies for deployment on edge computing devices were explored. The study also leveraged the high performance and low power consumption of embedded chips to process air quality data locally in real time. Experimental results showed that the system achieved 91.6% accuracy. In terms of precision and accuracy, our model improved by 8.3% and 10.6%, respectively, compared to traditional multilayer perceptron (MLP) and by 9.7% and 11.3%, respectively, compared to recurrent neural network (RNN), significantly enhancing the efficiency and reliability of air quality classification. Moreover, this research not only provides new perspectives for environmental monitoring and data processing but also elucidates the application of edge computing in intelligent environmental monitoring, which is crucial for promoting low-carbon development.