Low-Cost Alternatives for Urban Noise Nuisance Monitoring Using Wireless Sensor Networks

Noise pollution caused by vehicular traffic is a common problem in urban environments that has been shown to affect people´s health and children´s cognition. In the last decade, several studies have been conducted to assess this noise, by measuring the equivalent noise pressure level (called L_eq) to acquire an accurate sound map using wireless networks with acoustic sensors. However, even with similar values of L_eq, people can feel the noise differently according to its frequency characteristics. Thus, indexes, which can express people´s feelings by subjective measures, are required. In this paper, we analyze the suitability of using the psychoacoustic metrics given by the Zwicker´s model, instead of just only considering L_eq. The goal is to evaluate the hardware limitations of a low-cost wireless acoustic sensor network that is used to measure the annoyance, using two types of commercial and off-the-shelf sensor nodes, Tmote-Invent nodes and Raspberry Pi platforms. Moreover, to calculate the parameters using these platforms, different simplifications to the Zwicker´s model based on the specific features of road traffic noise are proposed. To validate the different alternatives, the aforementioned nodes are tested in a traffic congested area of Valencia City in a vertical and horizontal network deployment. Based on the results, it is observed that the Raspberry Pi platforms are a feasible low-cost alternative to increase the spatial-temporal resolution, whereas Tmote-Invent nodes do not confirm their suitability due to their limited memory and calibration issues.