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

Abstract

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 Leq) to acquite an accurate sound map using wireless networks with acoustic sensors. However, even with similar values of Leq, 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 psycho-acoustic metrics given by the Zwicker’s model, instead of just only considering Leq. 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, while Tmote-Invent nodes do not confirm their suitability due to their limited memory and calibration issues.