  Driven by the increasingly serious air pollution problem, the monitoring of
air quality has gained much attention in both theoretical studies and practical
implementations. In this paper, we present the architecture, implementation and
optimization of our own air quality sensing system, which provides real-time
and 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 is
to minimize the average joint error of the established real-time air quality
map, which involves data inference for the unmeasured data values. A deep
Q-learning solution has been proposed for the power control problem to
reasonably plan the sensing tasks of the power-limited sensing devices online.
A genetic algorithm has been designed for the location selection problem to
efficiently find the suitable locations to deploy limited number of sensing
devices. The performance of the proposed solutions are evaluated by
simulations, showing a significant performance gain when adopting both
strategies.
