# Analysis and Evaluation of Clustering Techniques Applied to Wireless Acoustics Sensor Network Data
## 4 Conclusions  
Noise pollution is a major concern in cities around the world and wireless acoustic sensor networks are being deployed to acquire information about sound pressure level in many locations and during long-term. Sharing data between administrations in a big data infrastructure, as the EU commission is promoting, can help to obtain better insights and create a common framework. Machine Learning techniques are being applied to learn and analyze these datasets. In this work, several machine learning clustering techniques have been applied to identify different acoustic environment patterns from sound pressure level datasets. A comparison of clustering techniques for modeling acoustic data from wireless acoustic sensor networks of the cities of Barcelona and Madrid (Spain) has been made. This evaluation has been performed using isolated data and federated data and three parameters as metrics: Connectivity, Dunn index, and Silhouette Width. From the results, it is observed that both Hierarchical Aggolomeration clustering and K-means have the best performance, in both federated and non-federated data. Therefore, they are the more suitable algorithms to fit environmental acoustics parameters, such as sound pressure levels during different periods of the day. In general, the Connectivity and Silhouette indexes tend to select a low amount of clusters, whereas the Dunn index suggests a large number of groups. Regarding the use case of noise monitoring and management of the noise plans, a small amount of clusters is recommended, therefore the Connectivity or Silhouette index has been used to select the optimal clustering algorithm. An external clustering validity index, the Chi index, has been also calculated, obtaining insight into the relevance of using federated data to do the clustering. More datasets will be incorporated in future works to further analyze the benefits of using federated datasets instead of isolated datasets. It has been shown that these techniques can help the local administrations to dynamically detect different patterns of sound pressure level behavior and update the definition of acoustic zones. Moreover, this information can be publicly shared with citizens to know about the acoustic typology of the area in which they live or are planning to buy a house, allowing them better decisions. Possible future work can continue this research along the following lines:  
1. Design a methodology for monitoring the evolution of the acoustic zones to be able to measure the effect of the actions carried out by the consistories included in their action plans. 2. Create an acoustic open data spaces for federated data to identify common clusters. 3. Develop an algorithm to identify the cluster in which belongs to a city spot considering only a small sample of data. Conceptualization, A.P. and J.M.N. ; data curation, A.P. ; investigation, A.P., J.M.N. and F.J.R. ; methodology, A.P. ; software, A.P. ; supervision, J.M.N. and F.J.R. ; validation, J.M.N. and A.P. ; writing--original draft, A.P. ; writing--review and editing, A.P., J.M.N. and F.J.R. All authors have read and agreed to the published version of the manuscript. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & \multicolumn{3}{c}{**(c) BCN 1 Data \(k\) = 4 K-means**} & \multicolumn{3}{c}{**(d) MAD 1 Data \(k\) = 2 Hierarchical**} \\
**Cluster** & **1** & **2** & **3** & **4** & **1** & **2** \\ \hline
1 & 18 & 12 & 0 & 0 & 12 & 0 \\
2 & 5 & 14 & 9 & 1 & 0 & 0 \\
3 & 0 & 1 & 0 & 10 & 9 & 10 \\  & & & & & Chi index: 1.104 & & Chi index: 0.714 \\ \hline \hline \end{tabular} 1 Abbreviations: MAD Madrid, BCN Barcelona.  
\end{table} 
Table 8: _Cont._  
**Funding:** Financial support for this research has been provided under grant PID2020-112827 GB-I00 funded by MCIN/AEI/10.13039/501100011033. **Institutional Review Board Statement:** Not applicable. **Informed Consent Statement:** Not applicable.