Multisource-Fusion-Enhanced Power-Efficient Sustainable Computing for Air Quality Monitoring

Given the severity of air pollution, air quality monitoring has become a crucial aspect of Artificial Intelligence of Things (AIoT) applications, providing essential information for forecasting air pollution. However, the training process for air quality monitoring models heavily relies on the high-performance computing resources, leading to significant energy consumption and associated carbon emissions. This contradicts the objectives of low-carbon and sustainable computing. This article proposes a new hybrid PM2.5 prediction model (NHPPM) for air quality monitoring to address the above challenges. NHPPM prioritizes energy efficiency while maintaining high prediction accuracy by integrating several power-efficient strategies. First, Wiener filtering is used to denoise the multisource air quality data enhancing the efficiency of the multisource data fusion. Second, variational mode decomposition (VMD) decomposes different components of the multisource air quality data, helping to identify and separate the most important factors affecting pollutants. This reduces the data needed for model training and leads to lower resource consumption. Kernel principal component analysis (KPCA) transforms the high-dimensional data into a lower-dimensional representation while retaining the critical information, further minimizing computational demands. Additionally, this article utilizes the informer deep learning model to analyse the trends in air quality data. The model’s effectiveness is validated through the ablation studies, performance evaluation experiments, and short- and long-term prediction experiments. The experimental results show that our model reduces the mean absolute error (MAE) and root mean-square error (RMSE) by 16.2% and 14.9%, respectively, compared to the existing PM2.5 prediction models. Furthermore, it reduces the energy consumption of the model training by 33.8%.