Improving urban noise monitoring opportunities via mobile crowd-sensing

Abstract

In the recent years, mobile devices pervasivity has boosted the diffusion of a novel sensing paradigm known as Mobile Crowd Sensing (MCS). In this paper, we propose a MCS-based system exploiting FIWARE middleware platform and allowing users to gather noise measurements (both opportunistically and participatory) in order to perform large-scale, low-cost and sufficiently accurate urban noise monitoring campaigns. Collected measurements are then aggregated, filtered and interpolated in order to provide city managers with an overview of the actual noise pollution levels in their cities. Specific noise abatement measures are suggested to city managers (in terms of both estimated noise reduction and average installation costs). The already performed field tests demonstrated the feasibility of the proposed approach.