Knowledge-based wireless sensors using sound pressure level for noise pollution monitoring

Over the last few years, there is a growing interest in monitoring noise pollution in urban areas and some recent studies have proposed the deployment of Wireless Sensor Networks for this task. Although the noise indicators defined by European Union directive 2002/49/EC can be calculated by sensor nodes, the noise perception is affected by subjective factors and there is not a direct correlation between the indicators and the subjective perception of noise. In this work, we present a mathematic algorithm for calculating the sound pressure level in a sensor node and a Fuzzy Noise Indicator that allows sensor nodes to infer the degree of subjective noise annoyance. Each sensor node executes an adapted Fuzzy Rule-Based System which has two inputs: a) the A-weighting equivalent noise level value and b) its persistence in time. The results show that the use of this Fuzzy Indicator helps to distinguish between situations with noise annoyance and other situations less annoying.