  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.
