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

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 le...

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Bibliographic Details
Published in:Applied sciences 2022-09, Vol.12 (17), p.8550
Main Authors: Pita, Antonio, Rodriguez, Francisco J., Navarro, Juan M.
Format: Article
Language:English
Subjects:
Acoustics
Algorithms
Background noise
Cities
Clustering
clustering algorithms
Councils
Datasets
environmental noise assessment
Internet of Things
knowledge discovery
Machine learning
Neural networks
Nodes
Noise pollution
Pollution control
Pressure
Sensors
Sound pressure
unsupervised learning
urban acoustic environment
Wireless networks
wireless sensor network data
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Summary: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.
ISSN:2076-3417
2076-3417
DOI:10.3390/app12178550