Deep matrix factorization models for estimation of missing data in a low-cost sensor network to measure air quality

According to the WHO, pollution is a worldwide public health problem. In Colombia, low-cost strategies for air quality monitoring have been implemented using wireless sensor networks (WSNs), which achieve a better spatial resolution than traditional sensor networks for a lower operating cost. Nevertheless, one of the recurrent issues of WSNs is the missing data due to environmental and location conditions, hindering data collection. Consequently, WSNs should have effective mechanisms to recover missing data, and matrix factorization (MF) has shown to be a solid alternative to solve this problem. This study proposes a novel MF technique with a neural network architecture (i.e., deep matrix factorization or DMF) to estimate missing particulate matter (PM) data in a WSN in Aburr ' a Valley, Colombia. We found that the model that included spatial-temporal features (using embedding layers) captured the behavior of the pollution measured at each node more efficiently, thus producing better estimations than standard matrix factorization and other variations of the model proposed here.

Automated summary

According to the WHO, pollution is a global public health issue. In Colombia, low-cost strategies using wireless sensor networks (WSNs) have been implemented for air quality monitoring. However, data missing is a common problem in WSNs due to environmental and location conditions. This study proposes a novel deep matrix factorization technique to estimate missing particulate matter data in a WSN, which outperforms standard matrix factorization and other variations of the model.