ALC2: When Active Learning Meets Compressive Crowdsensing for Urban Air Pollution Monitoring

As metropolises develop, air pollution has become a serious problem, especially in developing countries like China. Many governments and researchers have devoted themselves to tackling and solving this problem. With the proliferation of smartphones, mobile crowdsensing is becoming a promising paradigm for monitoring large-scale environmental phenomena. In a practical crowdsensing system, incentives should be provided to encourage the participation of rational smartphone users, because it incurs various costs on users to collect sensing data. However, monitoring fine-grained air pollution in a large urban area based on crowdsensing will lead to high payments, which makes designing an efficient incentive mechanism a challenging problem. Fortunately, compressive sensing (CS) has been proved as an effective technology to reduce the amount of collected data via exploiting the spatial correlations among sensing data. In this article, we employ CS in the air pollution monitoring application, in which only a sampled set of locations are selected to collect data and provide incentives to the participants, and air pollution concentrations in unselected locations are inferred via CS. We propose an active learning scheme, which iteratively selects valuable locations to collect sensing data. Moreover, an expectation maximization-based algorithm is designed to detect the contexts in which sensing data are collected, and an efficient incentive mechanism is provided to encourage users with low costs participating. Comprehensive simulations are conducted to demonstrate the performance of our proposed scheme.