# Analysis and Evaluation of Clustering Techniques Applied to Wireless Acoustics Sensor Network Data
## 2 Materials and Methods
### Data Sources  
This research has considered datasets from two different WASNs deployed in big cities, Barcelona and Madrid, Spain, and collected sound pressure level values. On one hand, the network of acoustics nodes deployed in Barcelona, denoted in this work by \(BCN_{X}\), by the city council during the last years consists of 86 sound sensors [18; 19]. The dataset used in this research was collected from 70 of the 86 sound sensors that were chosen for reasons of stability of the data over time and homogeneity in the spatial distribution of the nodes. The data were provided by the Barcelona City Council after a request from the authors. In the Acknowledgments section, the names of the data managers are indicated. As a summary, the data captured using Cesva TA120 [20] remote sonometers, considering international standards [21; 22], is aggregated and sent to a data platform called _Plataforma de Sensors i Actuadors de Barcelona_ [23]. A detailed explanation about the technological structure of the WASN and the data pipeline process involved can be found in [PERSON] et al. [18]. A description of the data source, the transformations carried out, the variables created, along with the distribution of the nodes is provided in a previous article of the authors [17]. On the other hand, the acoustic pollution monitoring network of the city of Madrid has 31 permanent stations, denoted in this work by \(MAD_{X}\), in charge of the control and continuous monitoring of the existing noise levels. [PERSON] et al. [24] described Madrid's WASN in detail showing how sound pressure level measurement dataset of these stations was retrieved from the acoustic pollution sensors and stored in a database management system platform that allows data analysts to work with the data in a structured way. The data are available on the Madrid council's open data portal [25]. In particular, data from recent years can be downloaded in the acoustic pollution data repository [26]. In the current research, only data from 2019 from both cities have been selected to explore a regular year period and avoid the pandemic period. More details regarding descriptive analysis and data processing can be found in previous authors' studies [17; 27] for both cities. Figure 1 shows the location of the chosen nodes in both cities. Figure 1: (**a**) Location of the of the 70 acoustic nodes in the city of Barcelona, Spain and (**b**) Location of the 31 acoustic nodes in the city of Madrid, Spain. These datasets are transformed into a normalized common structure that allows the comparison. The common structure is an structure table where rows represent each node with the following features: \(L_{\text{d2019}}\), \(L_{\text{e2019}}\), \(L_{\text{n2019}}\) and \(sd_{\text{2019}}(L_{\text{den1d}})\). Three first sound pressure level features have been selected considering the recommendations established in Directive 2002/49/EC [1] and to take into account levels during different time periods of the day. The last feature has been chosen to take into account long-term variation of the main parameter \(L_{\text{den}}\) in Directive 2002/49/EC [1]. These acoustic parameters are defined below. ISO 1996-2: 2017 [22] developed by the technical committee ISO/TC 43/SC 1 Noise describes how sound pressure levels intended as a basis for assessing environmental noise limits or comparison of scenarios in spatial studies can be determined. Determination can be performed by direct measurement and by extrapolation of measurement results through calculation. In this research, the definition, notations, and calculations performed over acoustic data follow the referred ISO [22]. As the sound pressure \(p(t)\) is measured continuously over a given time period \(T=[t_{1},t_{2}]\) for all \(t\in T\), to quantify the sound level on a single value using the equivalent sound pressure level in dB, denoted as \(L_{\text{eqT}}\). Equation (1) is used.