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Environmental pollution and the corresponding control measurements put in place to tackle it play a significant role in determining the actual quality of life in modern cities. Amongst the several pollutant that have to be faced on a daily basis, urban noise represent one of the most widely known for its already ascertained health-related issues. However, no systematic noise management and control activities are performed in the majority of European cities due to a series of limiting factors (e.g., expensive monitoring equipment, few available technician, scarce awareness of the problem in city managers). The recent advances in the Smart City model, which is being progressively adopted in many cities, nowadays offer multiple possibilities to improve the effectiveness in this area. The Mobile Crowd Sensing paradigm allows collecting data streams from smartphone built-in sensors on large geographical scales at no cost and without involving expert data captors, provided that an adequate IT infrastructure has been implemented to manage properly the gathered measurements. In this paper, we present an improved version of a MCS-based platform, named City Soundscape, which allows exploiting any Android-based device as a portable acoustic monitoring station and that offers city managers an effective and straightforward tool for planning Noise Reduction Interventions (NRIs) within their cities. The platform also now offers a new logical microservices architecture.

A considerable amount of research has addressed Internet of Things and connected communities. It is possible to exploit the sensing capabilities of connected communities, by leveraging the continuously growing use of cloud computing solutions and mobile devices. The pervasiveness of mobile sensors also enables the Mobile Crowd Sensing (MCS) paradigm, which aims at using mobile-embedded sensors to extend monitoring of multiple (environmental) phenomena in expansive urban areas. In this article, we discuss our approach with a cloud-based platform to pave the way for applying crowd sensing in urban scenarios. We have implemented a complete solution for environmental monitoring of several pollutants, like noise, air, electromagnetic fields, and so on in an urban area based on this paradigm. Through extensive experimentation, specifically on noise pollution, we show how the proposed infrastructure exhibits the ability to collect data from connected communities, and enables a seamless support of services needed for improving citizens’ quality of life and eventually helps city decision makers in urban planning.