GRC-Sensing: An Architecture to Measure Acoustic Pollution Based on Crowdsensing
Noise pollution is an emerging and challenging problem of all large metropolitan areas, affecting the health of citizens in multiple ways. Therefore, obtaining a detailed and real-time map of noise in cities becomes of the utmost importance for authorities to take preventive measures. Until now, the...
| Main Authors: | , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2018-08-01
|
| Series: | Sensors |
| Subjects: | |
| Online Access: | http://www.mdpi.com/1424-8220/18/8/2596 |
| _version_ | 1798034541680525312 |
|---|---|
| author | Willian Zamora Elsa Vera Carlos T. Calafate Juan-Carlos Cano Pietro Manzoni |
| author_facet | Willian Zamora Elsa Vera Carlos T. Calafate Juan-Carlos Cano Pietro Manzoni |
| author_sort | Willian Zamora |
| collection | DOAJ |
| description | Noise pollution is an emerging and challenging problem of all large metropolitan areas, affecting the health of citizens in multiple ways. Therefore, obtaining a detailed and real-time map of noise in cities becomes of the utmost importance for authorities to take preventive measures. Until now, these measurements were limited to occasional sampling made by specialized companies, that mainly focus on major roads. In this paper, we propose an alternative approach to this problem based on crowdsensing. Our proposed architecture empowers participating citizens by allowing them to seamlessly, and based on their context, sample the noise in their surrounding environment. This allows us to provide a global and detailed view of noise levels around the city, including places traditionally not monitored due to poor accessibility, even while using their vehicles. In the paper, we detail how the different relevant issues in our architecture, i.e., smartphone calibration, measurement adequacy, server design, and client–server interaction, were solved, and we have validated them in real scenarios to illustrate the potential of the solution achieved. |
| first_indexed | 2024-04-11T20:45:43Z |
| format | Article |
| id | doaj.art-deb52203f8ae4eac82a012060e9b7724 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-04-11T20:45:43Z |
| publishDate | 2018-08-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-deb52203f8ae4eac82a012060e9b77242022-12-22T04:04:02ZengMDPI AGSensors1424-82202018-08-01188259610.3390/s18082596s18082596GRC-Sensing: An Architecture to Measure Acoustic Pollution Based on CrowdsensingWillian Zamora0Elsa Vera1Carlos T. Calafate2Juan-Carlos Cano3Pietro Manzoni4Department of Computer Engineering (DISCA), Universitat Politècnica de València, 46022 Valencia, SpainDepartment of Computer Engineering (DISCA), Universitat Politècnica de València, 46022 Valencia, SpainDepartment of Computer Engineering (DISCA), Universitat Politècnica de València, 46022 Valencia, SpainDepartment of Computer Engineering (DISCA), Universitat Politècnica de València, 46022 Valencia, SpainDepartment of Computer Engineering (DISCA), Universitat Politècnica de València, 46022 Valencia, SpainNoise pollution is an emerging and challenging problem of all large metropolitan areas, affecting the health of citizens in multiple ways. Therefore, obtaining a detailed and real-time map of noise in cities becomes of the utmost importance for authorities to take preventive measures. Until now, these measurements were limited to occasional sampling made by specialized companies, that mainly focus on major roads. In this paper, we propose an alternative approach to this problem based on crowdsensing. Our proposed architecture empowers participating citizens by allowing them to seamlessly, and based on their context, sample the noise in their surrounding environment. This allows us to provide a global and detailed view of noise levels around the city, including places traditionally not monitored due to poor accessibility, even while using their vehicles. In the paper, we detail how the different relevant issues in our architecture, i.e., smartphone calibration, measurement adequacy, server design, and client–server interaction, were solved, and we have validated them in real scenarios to illustrate the potential of the solution achieved.http://www.mdpi.com/1424-8220/18/8/2596mobile crowdsensingsmartphonemachine learningnoise-sensingsmart citiesweka |
| spellingShingle | Willian Zamora Elsa Vera Carlos T. Calafate Juan-Carlos Cano Pietro Manzoni GRC-Sensing: An Architecture to Measure Acoustic Pollution Based on Crowdsensing Sensors mobile crowdsensing smartphone machine learning noise-sensing smart cities weka |
| title | GRC-Sensing: An Architecture to Measure Acoustic Pollution Based on Crowdsensing |
| title_full | GRC-Sensing: An Architecture to Measure Acoustic Pollution Based on Crowdsensing |
| title_fullStr | GRC-Sensing: An Architecture to Measure Acoustic Pollution Based on Crowdsensing |
| title_full_unstemmed | GRC-Sensing: An Architecture to Measure Acoustic Pollution Based on Crowdsensing |
| title_short | GRC-Sensing: An Architecture to Measure Acoustic Pollution Based on Crowdsensing |
| title_sort | grc sensing an architecture to measure acoustic pollution based on crowdsensing |
| topic | mobile crowdsensing smartphone machine learning noise-sensing smart cities weka |
| url | http://www.mdpi.com/1424-8220/18/8/2596 |
| work_keys_str_mv | AT willianzamora grcsensinganarchitecturetomeasureacousticpollutionbasedoncrowdsensing AT elsavera grcsensinganarchitecturetomeasureacousticpollutionbasedoncrowdsensing AT carlostcalafate grcsensinganarchitecturetomeasureacousticpollutionbasedoncrowdsensing AT juancarloscano grcsensinganarchitecturetomeasureacousticpollutionbasedoncrowdsensing AT pietromanzoni grcsensinganarchitecturetomeasureacousticpollutionbasedoncrowdsensing |