LoRaWAN Based Dynamic Noise Mapping with Machine Learning for Urban Noise Enforcement
This addresses urban noise enforcement for municipalities by improving dynamic noise mapping, though it is incremental as it builds on existing IoT and ML methods for a specific domain.
The paper tackled the problem of mapping transient non-traffic noise sources in urban areas, which are often missed by static noise maps, by proposing a dynamic noise mapping approach using LoRaWAN IoT data and machine learning for event and location prediction. The results showed that the system reduced map error caused by non-traffic sources by up to 51% and remained effective under significant packet losses.
Static noise maps depicting long-term noise levels over wide areas are valuable urban planning assets for municipalities in decreasing noise exposure of residents. However, non-traffic noise sources with transient behavior, which people complain frequently, are usually ignored by static maps. We propose here a dynamic noise mapping approach using the data collected via low-power wide-area network (LPWAN, specifically LoRaWAN) based internet of things (IoT) infrastructure, which is one of the most common communication backbones for smart cities. Noise mapping based on LPWAN is challenging due to the low data rates of these protocols. The proposed dynamic noise mapping approach diminishes the negative implications of data rate limitations using machine learning (ML) for event and location prediction of non-traffic sources based on the scarce data. The strength of these models lies in their consideration of the spatial variance in acoustic behavior caused by the buildings in urban settings. The effectiveness of the proposed method and the accuracy of the resulting dynamic maps are evaluated in field tests. The results show that the proposed system can decrease the map error caused by non-traffic sources up to 51% and can stay effective under significant packet losses.