# Noise Impact Analysis of School Environments Based on the Deployment of IoT Sensor Nodes
## 3 Results
### Sound Level Measurements and Processing  
As already mentioned, noise sampling at the four stations of the school infrastructures (i.e., buildings 10, 11, 20, and 21) considers one measurement every five minutes unless an event of high sound level (>85 dBA) is detected, and therefore, the sampling adjusts to one measurement every second for the whole duration that the event remains active. This is performed to identify and record events of high sound level that otherwise would not have been monitored by the user. Hence, high intensities were not well represented in the final sample, and position measures in the descriptive statistics could give larger--incorrectvalues than the real ones, as the statistical processing revealed. To correct this oversampling problem, the data were grouped into five-minute intervals, averages of these five-minute intervals were calculated, and new records were created, where each five-minute interval is represented by the average of the intervals of the measurements. In this case, we have concluded to capture records every five minutes, from which the weighting in favor of high noise levels has been eliminated. That generated a correct and consistent picture of the noise in the school complex. Table 2 below compares the average values before and after grouping and delineates the correction. Specifically, it is shown how grouping has mitigated the oversampling issue stated before (see column 5-min grouping--calibration frequency--where the improvement of the average values is significant compared to the no-grouping ones). It should be noted that the adoption of fine-grain frequency sampling for capturing the event of high noise is convenient to easily visualize and monitor its activity. However, debating on whether this scheme could be useful to apply for the whole duration of a school day, we concluded to reduce the sampling frequency and the subsequent data transmission due to energy-wise savings in the IoT sensor nodes and storage resource limitations in the cloud platform. Additionally, it should be emphasized that when a low-cost sensor exhibits significant deviation from actual measurements, and software adjustments fail to resolve the issue, it is more cost-effective to replace the sensor. This is because the cost of repairing and maintaining such a sensor is typically higher than purchasing a new one. It is denoted that Station 30, which was placed in the senior high school, did not cause noise overestimation due to the fact the sampling rate remained constant (one measurement every five minutes). Therefore, as can be noticed in Table 2, no deviations were identified. However, for clarity reasons, we also performed data annotation by grouping in five-minute intervals, which, of course, led to the same measurement results. One would debate why we did not follow the same approach for all stations. The main reason is that we wanted to provide detailed visualizations and study the spectrum of noise in case of an event for its whole duration (e.g., spikes, sensor errors, noise from machines, noise generated by pupils). It is obvious that sound can fluctuate greatly in a short period while appearing sporadically, so dense sampling is needed (5 min or more often if necessary). On the contrary, we do not expect significant changes, e.g., temperature within a few minutes; thus, we record values in longer periods (20 min) so that, without losing accuracy, we have short data flows in the network to minimize the storage requirements. Thus, sampling was configured at a different rate depending on the measurement variable. In order to derive homogeneous data that can be classified and compared accurately, an additional grouping was performed every hour (60 min). Therefore, we offline transformed data of all measuring stations and created representations for their hourly average values. It is denoted that this per-hour group was performed for the temperature, relative humidity, and PM values, and their school day variations were significantly low.