期刊
SENSORS
卷 22, 期 3, 页码 -出版社
MDPI
DOI: 10.3390/s22031000
关键词
Internet of Things; machine learning; smart cities; government facilities; air pollution; traffic monitoring
This paper proposes a solution to monitor city pollution by combining user feedback and real-time data. The system utilizes mobile devices and dedicated sensors to monitor air quality and traffic conditions, and presents the collected data through a mobile application and a website.
One of the main concerns of the last century is regarding the air pollution and its effects caused on human health. Its impact is particularly evident in cities and urban areas where governments are trying to mitigate its effects. Although different solutions have been already proposed, citizens continue to report bad conditions in the areas in which they live. This paper proposes a solution to support governments in monitoring the city pollution through the combination of user feedbacks/reports and real-time data acquired through dedicated mobile IoT sensors dynamically re-located by government officials to verify the reported conditions of specific areas. The mobile devices leverage on dedicated sensors to monitor the air quality and capture main roads traffic conditions through machine learning techniques. The system exposes a mobile application and a website to support the collection of citizens' reports and show gathered data to both institutions and end-users. A proof-of-concept of the proposed solution has been prototyped in a medium-sized university campus. Both the performance and functional validation have demonstrated the feasibility and the effectiveness of the system and allowed the definition of some lessons learned, as well as future works.
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