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

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