Incentive design for air pollution monitoring based on compressive crowdsensing

As air pollution is becoming a serious problem in developing nations, governments try to track and solve this problem by monitoring air pollution. With the proliferation of smartphones, mobile crowdsensing becomes a promising paradigm for monitoring fine-grained air pollution in urban areas. As existing studies have shown that pollutant concentrations have inherent spatiotemporal correlations, compressive sensing is an effective technology to reduce the amount of data collected through crowdsensing. In a practical crowdsensing application, incentives are expected by smartphone users for contributing sensing data. However, how to design incentives to collect high- quality sensing data with low costs is difficult in compressive crowdsensing. In this work, we propose an iterative scheme for the process of crowdsensing-based air pollution monitoring, where incentives are updated online according to the distribution of collected sensing data. Comprehensive simulations have been conducted to demonstrate the efficacy of our proposed scheme.