Enhanced air quality inference with mobile sensing attention mechanism: poster abstract

Mobile sensor networks are widely deployed for air quality monitoring. However, fine-grained pollution inference based on these systems is challenging. Specifically, diverse geospatial attributes in urban areas bring great spatial variations of the pollution field. Besides, the preprocessing on raw samples, such as discretization and averaging, leads to the lost of fine-grained information of mobile sensing. In this paper, we propose an inference algorithm with the attention mechanism to better capture high-frequency information in the pollution field. Furthermore, we introduce the sensing gradients in the attention network to utilize the high-granularity information from the mobile sensors. Evaluations on real-world dataset show that our model outperforms the state-of-the-art method by 13.15% ~ 27.04%.