Proceedings ArticleDOI
A deep autoencoder model for pollution map recovery with mobile sensing networks
Rui Ma,Ning Liu,Xiangxiang Xu,Yue Wang,Hae Young Noh,Pei Zhang,Lin Zhang +6 more
- pp 577-583
TLDR
This work proposes a deep autoencoder framework based inference algorithm that can obtain up to 2x performance improvement over existing methods, benefited from its high robustness against different background pollution level and accidental sensor errors.Abstract:
Air pollution is a global health threat. Nowadays, with the increasing amount of air pollution monitoring data from either conventional official stations or mobile sensing systems, the role of deep learning methods in pollution map recovery becomes gradually apparent. To address the challenges including the irregular sampling from mobile sensing and the non-interpretability of deep learning models, we proposed a deep autoencoder framework based inference algorithm. By separating the process of pollution generation and data sampling, this framework is able to deal with sampling under any irregular intervals in time and space. Also, we adopt a convolutional long short-term memory (ConvLSTM) structure to model the pollution generation after revealing its internal connections with an atmospheric dispersion model. Our algorithm is evaluated over a three-month real-world data collection in Tianjin, China. Results show our method can obtain up to 2x performance improvement over existing methods, benefited from its high robustness against different background pollution level and accidental sensor errors.read more
Citations
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The rise of low-cost sensing for managing air pollution in cities
Prashant Kumar,Lidia Morawska,Claudio Martani,George Biskos,George Biskos,George Biskos,Marina Neophytou,Silvana Di Sabatino,Margaret Bell,Leslie Norford,Rex Britter +10 more
TL;DR: In this article, the authors illustrate the drivers behind current rises in the use of low-cost sensors for air pollution management in cities, whilst addressing the major challenges for their effective implementation.
Journal ArticleDOI
PM₂.₅ Monitoring: Use Information Abundance Measurement and Wide and Deep Learning
TL;DR: Wang et al. as discussed by the authors developed a photograph-based monitoring model to estimate the real-time PM2.5 concentrations, overcoming currently popular electrochemical sensor-based PM 2.5 monitoring methods' shortcomings such as low density spatial distribution and time delay.
Journal ArticleDOI
Fine-Grained Air Pollution Inference with Mobile Sensing Systems: A Weather-Related Deep Autoencoder Model
TL;DR: This work proposes a deep autoencoder framework based inference algorithm that better captures the spatiotemporal dependencies in the pollution map with unevenly distributed samples than other real-time approaches, and presents a weather-related ConvLSTM to enable quasi real- time applications.
Journal ArticleDOI
Enhancing the Data Learning With Physical Knowledge in Fine-Grained Air Pollution Inference
TL;DR: A multi-task learning scheme, which combines the physical model and the data-driven model with both merits, enhances the data learning of a neural network with the aid of prior knowledge on atmospheric dispersion, and also controls the impact of the knowledge with a tunable weighting coefficient.
Posted Content
LOS: Local-Optimistic Scheduling of Periodic Model Training For Anomaly Detection on Sensor Data Streams in Meshed Edge Networks.
TL;DR: In this paper, the authors propose Local-Optimistic Scheduling (LOS), a method for executing periodic ML model training jobs in close proximity to the data sources, without overloading lightweight edge devices.
References
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