## 2 Material and Method
### The Network  
The used nodes in the network are based on Raspberry Pi B (RPi) platform. This platform is based on Broadcom BCM2835 System-on-Chip (SoC) [15], including an ARM1176 JZF-S 700 MHz processor [16], a Graphic Processing Unit (GPU) and 512 MB of RAM, with a SD slot card memory. A Logilink UA0053 USB sound card [17] was installed in the RPis in order to record the audio, with an electret omnidirectional microphone [18]. The frequency response of the electret microphone is nearly flat from 60-16000 KHz. Also a WiFi adapter TP-Link TL-WN725N [19] with IEEE 802.11 b/g/n standard has been introduced in the setup for communication purposes. This configuration allows each node to acquire several seconds of audio with 16 bits per sample (allowing a dynamic range of \(20\cdot log_{10}(2^{16})=96.33\) dB) at a sampling frequency of 22.05 kHz. The RPi have been placed in a protective housing, each with 3 Kodak 1.5 V KDLR20 batteries (19,500 mAh) [20]. A mean current consumption of 550 mAh (5 V) was measured, by disabling all unnecessary services in the operating system. It allows a life-time of 28 hours, considering an 80% efficiency for the batteries.  
A Raspbian Operating System [21] was installed in the RPi, which is a GNU/Linux version optimized for RPi hardware, based on a Debian distribution. To implement a Wireless Mesh Network, the Babel [22] routing protocol was used. This is a loop free, proactive protocol and it is based on distance vector algorithm. Babel is implemented in the Quagga [22] software suite. Also, a Cloud client called OwnCloud [23] was installed in each node, in order to gather the information from the different sensors into one system, connected to the Internet. The Owncloud server application was installed in a PC. This system allows storing and automatic synchronization of the information from the different nodes spatially distributed.  
A batch script has been programmed in order to setup an automatic measurement for 10 min [24; 25] every 3 h in each node. This allows a synchronized measurement within the established network. Figure 1 shows an RPi node with the electret microphone outside the box.  
Finally, a web-service based on OpenCPU [26] has been installed on a web server. This web-service allows programming in R. Its purpose is to harvest data from the cloud and process it using spatial statistics as explained later. Figure 2 shows the result of the node 4 (see location in Figure 3).  
In order to validate the sonometric records obtained with the RPi, a calibrated Type I standard sound level meter (CESVA SC-310 [27]) has been used to calibrate and check the different measurements at the same time of the calibrating experiments. Using previously digital recorded audio files of road traffic in one of the locations the mean and standard deviation of the \(L_{eqA,10s}\) during a 10 min period have analyzed. Before setting up the network test, a standard calibration using piston phones has been performed, to avoid the misalignment in the measured parameters due to the mismatches in microphones' sensitivities and frequency responses. The measurements were compared with the standard sound level meter and showed less than \(\pm 2\) dB error, both in short-term and long-term measures. This difference has been considered to adjust the values of the on-site measurements.  
Figure 1: Measurement node setup.  
Figure 2: OpenCPU framework to gather the noise pollution information.