A crowdsensing platform for real-time monitoring and analysis of noise pollution in smart cities

Abstract

This paper presents a crowdsensing platform for real-time monitoring and analysis of noise pollution in smart cities. The aim is to develop a comprehensive methodology and scalable infrastructure for measuring noise using mobile crowdsensing, storing and analysis of gathered data. The developed system consists of: 1) a mobile application for participatory and opportunistic crowdsensing of noise data from different microlocations, preprocessing of collected data, and sending data to the cloud, 2) a big data infrastructure for storing data and real-time big data analysis, and 3) a web application for decision support. Further, we have created a methodology that lets us select priority microlocations not typically covered by stationary measuring. The system has been evaluated experimentally; more than 4000 measurements were collected at five microlocations in the city of Belgrade, Serbia. Data was analysed in order to find the patterns that could serve for decision support to different stakeholders.