A crowdsensing-based framework for urban air quality decision support

Air pollution is considered a major health problem in urban areas. Small sensor technology integrated with smart phones can be widely used to collect air quality information in real time using mobile applications. By applying the concept of crowdsensing, citizens and authorities can be aware of exposure to pollution during their daily activities in urban areas. This paper describes an on-road air quality monitoring and control approach based on the crowdsensing paradigm. In addition to collecting air pollution data, we are exploring the possibility of using this technology to effectively detect critical situations and redistribute all information through a proactive decision support framework. This information can be combined with sensed air quality parameters for displaying, on an interactive map, the detected pollutants' concentrations using sensors attached to smart phones. The proposed framework provides users with real-time traffic and air quality information, traffic recommendations and notifications, and environmental conditions. Moreover, the authorities can use this system to improve urban mobility and traffic regulation. Such behavior and movements related to geographic information can provide a better understanding of the dynamics of a road network. In this work, we propose to combine the benefits of the crowdsensing paradigm with both machine learning and Big Data tools. An artificial neural networks model and the A* algorithm are used for air quality prediction and the least polluted path finding. All data processing tasks are performed over a Hadoop-based framework.