# Analysis and Evaluation of Clustering Techniques Applied to Wireless Acoustics Sensor Network Data
## 1 Introduction  
The European directive 2002/49/EC [1] encouraged agglomerations of people, namely, cities or groups of cities nearby, to create their strategic noise mapping (SNM) sharing the results with citizens. Moreover, the results of these noise maps led to the establishment of noise-reduction action plans where noise exposure protection zones are defined. To create performance reports with the data obtained in the strategic noise map and to define special noise protection areas within the city, data are usually analyzed by descriptive analysis, with basic statistics, such as the average or median of the defined noise indicator obtained for the overall assessment period. In general, using these statistics, two main types of areas are proposed relying on the places where values are higher than a certain recommended sound level, known as special regime areas, and others where their noise exposure is lower than the average, known as quiet areas. However, the acoustic environment of an area is a complex phenomenon that needs to be characterised not only by the noise levels in the area, but also by other properties such as its behavior in different time periods of the day and its long-term variation. Therefore, it would be interesting to explore the application of clustering techniques for the identification of areas with different behavior in relation to the noise environment. [PERSON] et al. [2] analyzed the methodological issues concerning the implementation of the directive across different countries of the European Union (EU), and dealing specifically with noise calculation and noise mapping, highlighting the implications of these issues for cross-country sharing of results. Moreover, a recent research [3] also summarizes the challenges to be faced by the EU Members and concludes that the opportunity to set up a common database of noise exposure based on common methods should be seized on time, encouraging local administrations to establish common frameworks. In the period 2021-2027, the European Commission will invest in a High Impact Project on Europeandata spaces and federated cloud infrastructures to encourage the establishment of EU-wide common, inter-operable data spaces in strategic sectors, such as mobility and health, and public administrations with data spaces initiatives, such as Gaia-X [4] and Federated European Infrastructure for Genomics data and Cancer Images data [5]. In the future, these data spaces can be used to join noise pollution data owned by public administrations to improve the health of citizens by the creation of more accurate predictive models, or obtaining better insights due to the more available data. In line with this trend, the application of unsupervised learning algorithms using federated data are proposed in this work to identify different acoustic environments that can help city managers to define personalized action plans for each behavior and share data in a common framework. In recent years, large cities are deploying Wireless Acoustic Sensor Networks (WASN), based on Internet of Things (IoT) technologies [6], to perform continuous monitoring of environmental acoustic parameters at many locations [7]. The acoustic nodes that compose these networks continuously capture information regarding the sound environment over long periods, generating a large amount of data. These acoustic data, together with further environmental data, such as water quality [8] or air pollution [9], are being used by city managers to make decisions and propose improvement actions. Moreover, this smart city system has given rise to the creation of the so-called dynamic noise maps where SNM is more often updated, each day for instance, by integrating data obtained from acoustic sensors and the application of predictive models of sound propagation in cities [10]. The improvement of SNM has been the goal of researchers, such as [PERSON] et al. [11] who concludes that colors add supplementary and more intuitive information on sound-scape to those provided by the acoustic parameters.