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Driven by the increasingly serious air pollution problem, the monitoring ofair quality has gained much attention in both theoretical studies and practicalimplementations. In this paper, we present the architecture, implementation andoptimization of our own air quality sensing system, which provides real-timeand fine-grained air quality map of the monitored area. As the major component,the optimization problem of our system is studied in detail. Our objective isto minimize the average joint error of the established real-time air qualitymap, which involves data inference for the unmeasured data values. A deepQ-learning solution has been proposed for the power control problem toreasonably plan the sensing tasks of the power-limited sensing devices online.A genetic algorithm has been designed for the location selection problem toefficiently find the suitable locations to deploy limited number of sensingdevices. The performance of the proposed solutions are evaluated bysimulations, showing a significant performance gain when adopting bothstrategies.

Real-Time Fine-Grained Air Quality Sensing Networks in Smart City: Design, Implementation and Optimization