Analysis and Evaluation of Clustering Techniques Applied to Wireless Acoustics Sensor Network Data

Pita, Antonio; Rodriguez, Francisco J.; Navarro, Juan M.

doi:10.3390/app12178550

Open AccessArticle

Analysis and Evaluation of Clustering Techniques Applied to Wireless Acoustics Sensor Network Data

by

Antonio Pita

,

Francisco J. Rodriguez

and

Juan M. Navarro

^*

Research Group in Advanced Telecommunications (GRITA), Universidad Católica de Murcia (UCAM), 30107 Guadalupe, Spain

^*

Author to whom correspondence should be addressed.

Appl. Sci. 2022, 12(17), 8550; https://doi.org/10.3390/app12178550

Submission received: 8 July 2022 / Revised: 16 August 2022 / Accepted: 21 August 2022 / Published: 26 August 2022

(This article belongs to the Special Issue Data Clustering: Algorithms and Applications)

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Abstract

Exposure to environmental noise is related to negative health effects. To prevent it, the city councils develop noise maps and action plans to identify, quantify, and decrease noise pollution. Smart cities are deploying wireless acoustic sensor networks that continuously gather the sound pressure level from many locations using acoustics nodes. These nodes provide very relevant updated information, both temporally and spatially, over the acoustic zones of the city. In this paper, the performance of several data clustering techniques is evaluated for discovering and analyzing different behavior patterns of the sound pressure level. A comparison of clustering techniques is carried out using noise data from two large cities, considering isolated and federated data. Experiments support that Hierarchical Agglomeration Clustering and K-means are the algorithms more appropriate to fit acoustics sound pressure level data.

Keywords: unsupervised learning; environmental noise assessment; urban acoustic environment; wireless sensor network data; knowledge discovery; clustering algorithms; data clustering

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MDPI and ACS Style

Pita, A.; Rodriguez, F.J.; Navarro, J.M. Analysis and Evaluation of Clustering Techniques Applied to Wireless Acoustics Sensor Network Data. Appl. Sci. 2022, 12, 8550. https://doi.org/10.3390/app12178550

AMA Style

Pita A, Rodriguez FJ, Navarro JM. Analysis and Evaluation of Clustering Techniques Applied to Wireless Acoustics Sensor Network Data. Applied Sciences. 2022; 12(17):8550. https://doi.org/10.3390/app12178550

Chicago/Turabian Style

Pita, Antonio, Francisco J. Rodriguez, and Juan M. Navarro. 2022. "Analysis and Evaluation of Clustering Techniques Applied to Wireless Acoustics Sensor Network Data" Applied Sciences 12, no. 17: 8550. https://doi.org/10.3390/app12178550

APA Style

Pita, A., Rodriguez, F. J., & Navarro, J. M. (2022). Analysis and Evaluation of Clustering Techniques Applied to Wireless Acoustics Sensor Network Data. Applied Sciences, 12(17), 8550. https://doi.org/10.3390/app12178550

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

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Analysis and Evaluation of Clustering Techniques Applied to Wireless Acoustics Sensor Network Data

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