An IoT-Aware Solution to Support Governments in Air Pollution Monitoring Based on the Combination of Real-Time Data and Citizen Feedback

Abstract

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.

Internet of Things (IoT)-based indoor plant care system

“…Indoor pollution can be caused by the entry of vehicles either to load or unload goods in factories, workshops, or warehouses, or simply to park them in garages when these are inside homes [21]. One of the fields of application of IoTS is the monitoring of the concentration levels of pollutant gases using sensors [7,11,22] and, as mentioned above, one of the practices that have been implemented to help decontaminate indoor air and improve its quality is the use of ornamental plants [2,6,37]. These two practices, i.e., technology-based surveillance and the use of ornamental plants, can be combined and complement each other.…”

A Radical Safety Measure for Identifying Environmental Changes Using Machine Learning Algorithms

“…It keeps track of the differences between two successive time series [10], regardless of whether or not their recommendations are taken into consideration [30][31][32][33][34][35][36][37][38]. Specifically, it demonstrates that the standard and ultimate differences between a prediction and actual gathered data are the same rolling average [11] and the relative disparities in the equations for the MAE computation are represented in Equation (11).…”

Community- and Data-Driven Services for Multi-Policy Pedestrian Routing

“…The influence of the users' perception, this time related to air pollution, has also been approached and implemented as a mobile application and a website that support report gathering from users, as well as visualization of these reports [24]. The application uses dedicated sensors for air quality monitoring, and the data acquired from them are processed with machine learning techniques.…”