## 1 Introduction  
Real-time mapping systems for noise pollution monitoring is a challenging issue that has already been addressed in several research works [1; 2]. In this context, the study of noise pollution in urban environments aimed at developing networked monitoring systems combined with spatial models for predicting sound pressure levels is not straightforward. Noise pollution is a common problem in urban environments. It affects human behavior, health and even children's cognition. Recognizing this as a major issue, the European Commission adopted a directive [3], requiring main cities (with more than 100,000 inhabitants) to gather real world data on noise exposure in order to produce local action plans and to provide accurate mappings of noise pollution levels. The traditional way of conducting noise measurements is by collecting noise samples manually, but this technique has many drawbacks. On the one hand, only local and sparse measurements are taken. On the other hand, it is expensive due to the measuring equipment and personnel costs. However, the European Commission recommends for higher granularity of noise data both in space and time. In this scenario, Wireless Sensor Networks (WSNs) represent an alternative that can overcome the drawbacks of the current noise data collection procedure. These WSNs are composed of small autonomous nodes with sensing capabilities. Each node has its own power supply, processing unit and memory. The nodes communicate using multi-hop routing protocols and at least one node (named sink) acts as a gateway for external connection. In the last decade, several studies have been conducted using WSN for noise pollution monitoring. All of these studies are based on the equivalent sound pressure level over time \(T\). Several works have considered the use of WASNs for noise monitoring. In [4; 5], authors have evaluated a WASN using Tmote-Sky motes [6] and Tmote Invent (TmI), to monitor traffic noise using the equivalent level, \(L_{eq,T}\) and to count the number and type of vehicles. In this deployment, they used a sampling frequency of 8 kHz. In their study, they found that Tmote-Sky had excessive self-noise and TmI (with an integrated microphone) had apparently good audio features. In the references, the authors do not provide a specific calibration. In [7; 8], a WASN deployment in Ostrobothnia (Finland) is discussed. In these references, the authors report different tests to evaluate the noise impact. They measured the \(L_{eq,T}\) with \(T=125\) ms using a sampling frequency of 33 kHz, with 14 calibrated motes (MicaZ from Crossbow -now this company is MEMSIC- with an ad hoc acquisition circuitry to allow a dynamic range of 60 dB), globally synchronized during 96 h with good results. Other references such as [1; 9; 10] have used mobile phones for noise pollution monitoring. Although the results are interesting, in our opinion, there is a lack of information about the recording conditions which avoids getting accurate noise measurements. When evaluating noise parameters, the location of the measuring devices should follow specific rules [3]. In all of the previous references [1; 4; 5; 6; 7; 8; 9; 10], the measurements are based on the \(L_{eq,T}\) or its A-weighted version (ITU-R 468), \(L_{eq,A,T}\). These measurements apply a frequency-selective filter that picks up the frequency range around 3-6 kHz, where the human ear is most sensitive. These parameters are measured in dB and dBA, respectively. The data collection procedure from the sensor nodes must also be considered. In the literature [11], there are several approaches that allow a good data collection. These protocols are associated not only to the routing protocols of the information to the sink, but also to the application to represent the information which can be presented as a web-service. Another issue to consider in the data information system is the statistical representation of the noise data information oriented to detect problematic areas in real time.