Crowd-Sourced Data Collection for Urban Monitoring via Mobile Sensors

Abstract

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.

Mobile crowd-sensing as a resource for contextualized urban public policies: a study using three use cases on noise and soundscape monitoring

“…Still, MPS appears as a promising complementary technology to the above considering the massive use of smartphones that embed increasingly accurate microphones (Kardous & Shaw, 2015) and location systems (Longo, Zappatore, Bochicchio, & Navathe, 2017). In recent years, and notably since most cities have engaged in smart projects, many studies have pointed out the opportunities and issues of MPS contributions for a better knowledge of the urban environment.…”

Matching Technological & Societal Innovations: The Social Design of a Mobile Collaborative App for Urban Noise Monitoring

“…A usage-centric approach to the design of a new Ambiciti interface. mediation place [18]. It must allow the user to negotiate (it is the user experience) the balance between features and protocols, culturally anchored subjective practices and a societal context.…”

Smart Cities Big Data Algorithms for Sensors Location

“…Also, a paradigm proposed in smart cities is the mobile crowd sensing (MCS), which focuses on using mobile integrated sensors to monitor multiple environmental phenomena, such as noise, air, or electromagnetic fields in the environment [11]. In this same topic, a system of pollution warning services was developed for smart cities [12], to notify the user of the concentration of pollution in the place where they are located, through mobile devices that measure the quality of the air.…”

“…In this case, mobile devices measure air quality [10]. In [11], an implementation of smart sensors was presented to monitor air quality where the tracking variables included dust particles (PM 10 ), carbon monoxide (CO), carbon dioxide (CO 2 ), noise level (dB), and ozone (O 3 ), with the aim to keep people informed in real-time through the IoT.…”