Summary of Applications Of Machine Learning and Iot For Outdoor Air Pollution Monitoring and Prediction: a Systematic Literature Review, by Ihsane Gryech et al.
Applications of machine learning and IoT for Outdoor Air Pollution Monitoring and Prediction: A Systematic Literature Review
by Ihsane Gryech, Chaimae Assad, Mounir Ghogho, Abdellatif Kobbane
First submitted to arxiv on: 3 Jan 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper systematically reviews machine learning and Internet of Things (IoT) applications for outdoor air pollution prediction. The authors analyze 37 papers, highlighting the methods used, including time series, feature-based, and spatio-temporal approaches. The review finds limitations in current literature, such as lack of data coverage, diversity, and context-specific features. The study proposes directions for future research, emphasizing practical implications in healthcare, urban planning, global synergy, and smart cities. The authors also conduct a cost-based analysis, comparing high-cost monitoring with low-cost IoT and hybrid-enabled prediction methods. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Air pollution is a big problem that kills millions of people every year. Scientists are working on ways to predict air pollution using machine learning and the Internet of Things (IoT). This paper looks at lots of research studies to see what’s been done so far. They found that many studies have some limitations, like not having enough data or not considering local factors. The researchers suggest ways to improve these predictions in the future, which could help cities and healthcare systems make better decisions. |
Keywords
* Artificial intelligence * Machine learning * Time series
