A big data framework for urban noise analysis and management in smart cities

Environmental pollution monitoring is a major concern in the development of smart cities. Nowadays, urban noise is one of the most relevant pollutants, so many networks of acoustic sensors have been deployed to measure sound pressure levels at various locations. These acoustic sensors collect huge a...

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Detalhes bibliográficos
Autores: Navarro Ruiz, Juan Miguel, Tomas Gabarron, Juan Bautista, Escolano, José
Formato: artículo
Fecha de publicación:2017
País:España
Recursos:Universidad Católica San Antonio de Murcia (UCAM)
Repositorio:RIUCAM. Repositorio Institucional de la Universidad Católica San Antonio de Murcia
OAI Identifier:oai:repositorio.ucam.edu:10952/9349
Acesso em linha:http://hdl.handle.net/10952/9349
Access Level:acceso abierto
Palavra-chave:Big data
Acoustics
Noise pollution
Sound pressure levels
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spelling A big data framework for urban noise analysis and management in smart citiesNavarro Ruiz, Juan MiguelTomas Gabarron, Juan BautistaEscolano, JoséBig dataAcousticsNoise pollutionSound pressure levelsEnvironmental pollution monitoring is a major concern in the development of smart cities. Nowadays, urban noise is one of the most relevant pollutants, so many networks of acoustic sensors have been deployed to measure sound pressure levels at various locations. These acoustic sensors collect huge amounts of data, which can be helpful to manage noise events in urban planning. In this paper, a big data framework is proposed to properly analyse the considerably large amounts of noise monitoring data and obtain useful information for urban planning. A map and reduce approach is proposed to process the massive data captured from acoustic sensor networks, mobile phones and open data platforms. Using the map and reduce model, several statistical environmental acoustic parameters, including both temporal and spatial indices, can be calculated. As an example application, two algorithms are implemented to evaluate both day-evening-night equivalent levels (Lden) and percentile levels (Ln). An experimental case with data obtained from the Dublin open data platform shows the benefits of this framework for urban noise analysis and management.Ciencias AmbientalesIngeniería, Industria y ConstrucciónEscuela Politécnica2017info:eu-repo/semantics/articlehttp://hdl.handle.net/10952/9349reponame:RIUCAM. Repositorio Institucional de la Universidad Católica San Antonio de Murciainstname:Universidad Católica San Antonio de Murcia (UCAM)Inglésinfo:eu-repo/grantAgreement/MINECO/TIN2016-78799-P/info:eu-repo/semantics/openAccessoai:repositorio.ucam.edu:10952/93492026-06-07T18:35:21Z
dc.title.none.fl_str_mv A big data framework for urban noise analysis and management in smart cities
title A big data framework for urban noise analysis and management in smart cities
spellingShingle A big data framework for urban noise analysis and management in smart cities
Navarro Ruiz, Juan Miguel
Big data
Acoustics
Noise pollution
Sound pressure levels
title_short A big data framework for urban noise analysis and management in smart cities
title_full A big data framework for urban noise analysis and management in smart cities
title_fullStr A big data framework for urban noise analysis and management in smart cities
title_full_unstemmed A big data framework for urban noise analysis and management in smart cities
title_sort A big data framework for urban noise analysis and management in smart cities
dc.creator.none.fl_str_mv Navarro Ruiz, Juan Miguel
Tomas Gabarron, Juan Bautista
Escolano, José
author Navarro Ruiz, Juan Miguel
author_facet Navarro Ruiz, Juan Miguel
Tomas Gabarron, Juan Bautista
Escolano, José
author_role author
author2 Tomas Gabarron, Juan Bautista
Escolano, José
author2_role author
author
dc.subject.none.fl_str_mv Big data
Acoustics
Noise pollution
Sound pressure levels
topic Big data
Acoustics
Noise pollution
Sound pressure levels
description Environmental pollution monitoring is a major concern in the development of smart cities. Nowadays, urban noise is one of the most relevant pollutants, so many networks of acoustic sensors have been deployed to measure sound pressure levels at various locations. These acoustic sensors collect huge amounts of data, which can be helpful to manage noise events in urban planning. In this paper, a big data framework is proposed to properly analyse the considerably large amounts of noise monitoring data and obtain useful information for urban planning. A map and reduce approach is proposed to process the massive data captured from acoustic sensor networks, mobile phones and open data platforms. Using the map and reduce model, several statistical environmental acoustic parameters, including both temporal and spatial indices, can be calculated. As an example application, two algorithms are implemented to evaluate both day-evening-night equivalent levels (Lden) and percentile levels (Ln). An experimental case with data obtained from the Dublin open data platform shows the benefits of this framework for urban noise analysis and management.
publishDate 2017
dc.date.none.fl_str_mv 2017
dc.type.none.fl_str_mv info:eu-repo/semantics/article
format article
dc.identifier.none.fl_str_mv http://hdl.handle.net/10952/9349
url http://hdl.handle.net/10952/9349
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.relation.none.fl_str_mv info:eu-repo/grantAgreement/MINECO/TIN2016-78799-P/
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.source.none.fl_str_mv reponame:RIUCAM. Repositorio Institucional de la Universidad Católica San Antonio de Murcia
instname:Universidad Católica San Antonio de Murcia (UCAM)
instname_str Universidad Católica San Antonio de Murcia (UCAM)
reponame_str RIUCAM. Repositorio Institucional de la Universidad Católica San Antonio de Murcia
collection RIUCAM. Repositorio Institucional de la Universidad Católica San Antonio de Murcia
repository.name.fl_str_mv
repository.mail.fl_str_mv
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