Prototipo experimental para la monitorización de ruido en tiempo real en entornos urbanos

Zusammenfassung

Many studies have demonstrated that exposure to environmental noise can cause problems in people, such as hypertension, hearing loss, stress, insomnia and low productivity. According to the European Union (EU), noise pollution is a serious risk to public health and productivity. For this reason, the EC approved a policy to reduce environmental noise in 1996. This policy resulted in Directive 2002/49/EC, which requires member states to provide accurate mappings of noise levels throughout all urban centers with more than 250,000 inhabitants, main airports, major railways and roads. Noise maps can be computed using commercial sound level meters, measuring the equivalent sound level. These devices have a number of inherent technological disadvantages, such as the lack of real-time data. Therefore, it is not possible to achieve a temporal evolution of the measured data. This work proposes to use Wireless Sensor Networks (WSNs) to create accurate maps of noise levels in real time and space (streets, airports, etc.). WSNs are networks composed of a large number of sensor nodes, low cost devices with limited computational capability and communications, and normally powered by batteries. In this sense, we present a sensor prototype, which uses an algorithm based on frequency domain analysis for calculating the noise indicator. To the best of our knowledge, the approach presented in this paper is the first one to propose an algorithm based on frequency domain analysis to calculate noise indicators in a sensor node of a WSN. In order to verify the performance of the proposed system, several experiments have been carried-out using a variety of scenarios to compare the differences between the noise indicators calculated by the sensor and those from a commercial sound level meter. The results show that the difference between the sensor and the SLM is less than 1%, and it demonstrates that the proposed prototype can be used to create noise maps in real time for urban areas.