A cost aware crowdsensing approach for urban air quality sensing and computing

Nowadays, public authorities and citizens are more concerned about monitoring and managing urban air quality since it greatly affects the quality of life and well-being. Traditional practices and studies focused on sensing air quality by leveraging either fixed monitoring stations or dedicated mobile sensing equipment with expensive sensing costs. But recently, the vast distribution of the sensor-rich mobile devices carried by participants have made a new sensing paradigm possible, namely Mobile Crowdsensing. In this paper, we consider the inconsistency of sensing a sample in different subareas, combine compressive sensing and crowdsensing in the air quality applications, and correspondingly propose a cost aware crowdsensing framework for air quality sensing consisting of six stages: information modeling, cost estimation, cell selection, quality assessment, data inference and sensing data computing. Significantly, we present three cost aware task allocation strategies. Evaluations are conducted on real PM2.5 monitoring data-set. Our task allocation method based on the novel framework can remarkably reduce the inference errors with less sensing cost compared to the baselines, which demonstrates the performance of our proposed scheme.