## 4 Conclusions  
In this paper, a distributed noise measurement system based on Internet of Things (IoT) technology developed with Raspberry Pi nodes and a data collection system based in OpenCPU framework, which is related to the R statistical programming language, has been tested to measure the hourly sound pressure level evolution.  
Figure 8: Interpolated noise levels per time period: (**a**) 7–10 h; (**b**) 10–13 h; (**c**) 13–16 h; (**d**) 16–19 h and (**e**) 19–22 h.  
Figure 9: Interpolated time-average noise levels on a map layer.  
With this data, the application of a geostatistical method for interpolating spatial noise levels in a small-sized city in Valencia has been presented. The methodology makes the determination of the simultaneous spatial variation of noise levels in different time periods possible. From the results obtained, some notable conclusions can be extracted. One of them is that the kriging method has been proven as an efficient method to show noise level information in real-time. In addition, the residuals are spatially correlated in most time periods, except for time period 5. Perhaps, this fact is due to the spatial variation of the noise levels in the whole measuring grid in this period. For this reason, the last time period has been excluded in the model, due to the spatial non-correlation of the residuals in the period between 19 h and 22 h.  
The goodness of the model in each time period has been checked from the predictions of the noise level in the 10 locations of the validation set (i.e., 13% of the nodes). The results have proven the validity of the spatial model in four time periods. This can be also checked within the cross validation results.  
The methodology allows for representing these noise models, enabling the localization of critical areas in different time periods and the prediction of noise levels in different parts of the city. It also allows for showing that the noise levels are highly dependent on the spatial statistic model defined [38].  
This method differs greatly from the issue of the subjective assessment of the noise annoyance, which poses quite a problem for those who are interested in developing and standardising units of measurement that convey the extent of the intensity of noise. It is also well-known that the decibel and the A-weighted system were developed precisely for this purpose, but they are far from being perfect [39]. At this point, it is worth mentioning that great efforts are being made for the soundscape description that is being standardised in ISO 12913 [40].  
Further work on these issues is currently being done by increasing the number of measuring nodes in the WASN to measure urban noise, also previewed to collect full day measurements to compute parameters \(L_{d}\), \(L_{e}\) and \(L_{den}\). This technology in combination with the explained methodology is a promising powerful tool for real-time distributed noise measurement and assessment.  
Authors would like to thank the Spanish Ministry of Economy and Innovation for the funding, coming from the grant TEC2012-37945-C02-02 and the grant BIA2016-76957-C3-1-R.  
[PERSON], [PERSON], [PERSON] and [PERSON] conceived and designed the network and the experiments; [PERSON], [PERSON] and [PERSON] performed the experiments; [PERSON] and [PERSON] analyzed the data and made all the statical analysis; [PERSON] and [PERSON] contributed with the WASN nodes; [PERSON] wrote the paper.  
The authors declare no conflict of interest. The funding sponsors had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, and in the decision to publish the results.