Summary of Lorawan Based Dynamic Noise Mapping with Machine Learning For Urban Noise Enforcement, by H. Emre Erdem et al.
LoRaWAN Based Dynamic Noise Mapping with Machine Learning for Urban Noise Enforcement
by H. Emre Erdem, Henry Leung
First submitted to arxiv on: 30 Jul 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Networking and Internet Architecture (cs.NI)
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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 The paper proposes a novel approach to dynamic noise mapping, which is crucial for urban planning as it enables municipalities to decrease noise exposure of residents. The current static noise maps ignore transient non-traffic noise sources that people complain about frequently. To overcome this limitation, the authors utilize low-power wide-area network (LPWAN) and internet of things (IoT) infrastructure, commonly found in smart cities, to collect data on noise levels. However, LPWAN’s low data rates pose a challenge, which is addressed by applying machine learning (ML) for event and location prediction of non-traffic sources based on limited data. The proposed method considers the spatial variance in acoustic behavior caused by urban buildings, leading to more accurate dynamic maps. In field tests, the system achieved a 51% reduction in map error caused by non-traffic sources, even under significant packet losses. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about creating better noise maps for cities. Noise maps help cities decide how to reduce noise pollution and make life better for residents. Right now, most noise maps only show long-term noise levels over a small area, but they ignore the annoying sounds that happen suddenly and are hard to predict. To fix this, the authors use special technology that collects data on noise levels in real-time. This allows them to create more accurate noise maps that take into account the buildings and layout of the city. They tested their system and found it could reduce errors by 51% compared to traditional methods. |
Keywords
* Artificial intelligence * Machine learning
