LoRaWAN Based Dynamic Noise Mapping with Machine Learning for Urban Noise Enforcement

• URL: http://arxiv.org/abs/2407.21204v1
• Date: Tue, 30 Jul 2024 21:40:12 GMT
• Title: LoRaWAN Based Dynamic Noise Mapping with Machine Learning for Urban Noise Enforcement
• Authors: H. Emre Erdem, Henry Leung,
• Abstract summary: Static noise maps depicting long-term noise levels over wide areas are valuable urban planning assets for municipalities. 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) based internet of things (IoT) infrastructure.
• Score: 8.010966370223985
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: 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.

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