Next Article in Journal
Distributed Deformation Monitoring for a Single-Cell Box Girder Based on Distributed Long-Gage Fiber Bragg Grating Sensors
Previous Article in Journal
PRG: A Distance Measurement Algorithm Based on Phase Regeneration
Previous Article in Special Issue
The Shared Bicycle and Its Network—Internet of Shared Bicycle (IoSB): A Review and Survey
Article Menu

Export Article

Open AccessArticle
Sensors 2018, 18(8), 2596; https://doi.org/10.3390/s18082596

GRC-Sensing: An Architecture to Measure Acoustic Pollution Based on Crowdsensing

1
Department of Computer Engineering (DISCA), Universitat Politècnica de València, 46022 Valencia, Spain
2
Faculty of Computer Science (FACCI), Universidad Laica Eloy Alfaro de Manabí, 130802 Manta, Ecuador
*
Author to whom correspondence should be addressed.
Received: 2 July 2018 / Revised: 24 July 2018 / Accepted: 3 August 2018 / Published: 8 August 2018
(This article belongs to the Special Issue Crowd-Sensing and Remote Sensing Technologies for Smart Cities)
View Full-Text   |   Download PDF [12880 KB, uploaded 8 August 2018]   |  

Abstract

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. View Full-Text
Keywords: mobile crowdsensing; smartphone; machine learning; noise-sensing; smart cities; weka mobile crowdsensing; smartphone; machine learning; noise-sensing; smart cities; weka
Figures

Figure 1

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).
SciFeed

Share & Cite This Article

MDPI and ACS Style

Zamora, W.; Vera, E.; Calafate, C.T.; Cano, J.-C.; Manzoni, P. GRC-Sensing: An Architecture to Measure Acoustic Pollution Based on Crowdsensing. Sensors 2018, 18, 2596.

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Related Articles

Article Metrics

Article Access Statistics

1

Comments

[Return to top]
Sensors EISSN 1424-8220 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top