## 4 Results
### Noise Pollution Monitoring  
One of the most important features of the protocol and the multi-agent system deployed on the sensor nodes is the versatility of all of the components. Therefore, it is possible to implement small applications inside a measurement manager of the Application Control Agent without changing the protocol. As an example, we implemented an application for the monitoring of environmental noise pollution in an urban area. There is a growing interest in monitoring environmental pollution parameters in urban areas. Recent studies [27] have demonstrated that exposure to environmental noise increases the risk of hypertension, hearing loss, and sleep disorders, and that it negatively influences productivity and social behavior. Thus, European Directive 2002/49/EC requires member states to provide accurate mapping of noise levels throughout all urban centers with more than 250,000 citizens. Some authors have proposed the deployment of WSNs to monitor noise pollution and create maps of noise levels [28-30]. The noise indicator used as a criteria for the assessment of occupational noise, according to ISO standard 1999 [31], and used to create noise maps is called equivalent continuous sound pressure level, _Leq._ and is defined as:  
\[L_{eq.T}=10\log{{}_{10}}(\frac{1}{T}\int\limits_{0}^{T}\frac{p\ (t)^{2}}{p_{o}^{2}}dt \tag{1}\]  
where _p(t)_ represents the root mean square instantaneous sound pressure produced by an acoustic wave and _p\({}_{o}\)_ is a reference value corresponding to the minimal audible acoustic signal for a human at 1 KHz, \(2\ \times 10^{-5}\) Pa. However, to simulate the frequency response of the human, _p(t)_ is passed through a filtering stage, A-weighting, which is a commonly used frequency weighting that reflects the loudness perceived by human. Thus, we used the indicator L\({}_{\text{Aeq. }}\) measured in decibels (dBA), which captures the A-weighted sound pressure level of a constant noise source over the time interval T, which has the same acoustic energy as the actual varying sound pressure over the same interval [32]. The A-weighting equivalent continuous sound pressure level is defined as:  
\[L_{Aeq,T}=10\log{{}_{10}}(\frac{1}{T}\int\limits_{0}^{T}\frac{p_{A}(t)^{2}}{p _{o}^{2}}dt \tag{2}\] \begin{table}
\begin{tabular}{l c c c} \hline
**Times** & **Battery charge** & **Power Consumption** & **Ratio per cycle** \\ \hline
0 & 97\% & - & - \\
1020 & 90\% & 7\% & 0.006863 \\
3952 & 70\% & 20\% & 0.006821 \\
4314 & 68\% & 2\% & 0.005525 \\
5572 & 60\% & 8\% & 0.006359 \\
7020 & 51\% & 9\% & 0.006215 \\ \hline \end{tabular}
\end{table} 
Table 5: Power consumption per awake cycle. To perform the experiment related to the measurement manager, we designed and implemented a simple analog circuit, equipped with an electret microphone, which was incorporated with a Sun SPOT sensor, as shown in Figure 6. In addition, we used a base station and a personal computer. The Sun SPOT sensor with the analog circuit was deployed close (about 10 meters) to an urban road. The application collects raw acoustic samples at a rate of 8 KHz, calculates the L\({}_{\text{Aeq,T}}\) with a temporal granularity T of 125 ms (using 1,000 samples) each second, and transmits the value to the base station. Figure 7 shows the A-weighting equivalent noise level values measured by the sensor node for 166 minutes (from 4 p.m. to 6:45 p.m.). The values are generally around 48-52 dBA, which indicates that the noise pollution is moderate. Nevertheless, there are some times where the noise levels are higher; these correspond to passing vehicles. Figure 6: Sun SPOT node with the analog circuit. Figure 7: A-weighting equivalent noise level values measured by the sensor node. Figure 8 shows the A-weighting equivalent noise level values collected during the first three minutes of the experiment. There are three peaks, which represent three vehicles in transit, of which the first two were closer than the third.