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The recent expansion of wireless sensor networks (WSN) in urban cities has led to possibility of generation of large amounts of information data from the environmental monitoring systems among which one of the most concerning is the excessive urban noise pollution. As opposed to the conventional noise mapping procedures that involve costly and time-consuming measurement process with a traditional high-priced noise level meter, the low-cost wireless sensor networks provide a method for achieving data collection and analysis with a higher level of granularity. This paper presents a wireless noise sensor unit design for continuous environmental noise level monitoring as a framework for the realization of the Internet of Things (IoT) and „Smart city" concept. The concept involves the complete noise data information system, from sensor structure to data visualization and data analysis. The overall design, characteristics and performance of the sensing system for continuous measuring urban noise pollution is discussed. [ABSTRACT FROM AUTHOR]

Continuous noise monitoring based on mobile systems could provide a quick feedback to assess the effect of policies implemented by authorities to mitigate noise pollution. The present research verifies that mobile measurements taken along a main street and aggregated in time and space can accurately estimate noise levels at static points. As a consequence, mobile sensors would be suitable to build, and continuously update, noise maps. Furthermore, the experiment computes the optimum aggregation distance of the mobile measurements. To perform the mobile noise measurements, a low-cost noise sensor with an integrated GPS was mounted on a bicycle. One hour worth of measurements was taken along a main avenue with the mobile receiver simultaneously to 6 static measurement points. For the mobile receiver, the L Aeq was computed aggregating samples within a radius from 1 m to 100 m around the static measurement points. Then, the error between the aggregated L Aeq of the mobile and the static receivers for the same time period was computed. It is observed that the RMSE and the measurement uncertainties decrease as the aggregation distance increases, having a minimum at an aggregation radius of 33 m and reaching a stabilization due to the constant traffic of the studied street. • Noise assessment based on mobile sampling reduces the resources for noise sampling process. • Spatial aggregation of mobile samples decreases the estimation error and uncertainties. • An optimal aggregation radius was found based on three different approaches (≈33 ± 1 m). • Mobile measurements increase the spatio-temporal coverage of noise pollution assessment. [ABSTRACT FROM AUTHOR]