In this paper, an end-to-end system for ambient real-time air
quality monitoring and prediction is presented. The system has
two main components, the multigas monitoring stations and
the M2M platform. Four solar powered multigas monitoring
stations have been deployed and the data of four months have
been collected, cleaned, and analyzed. The monitoring stations
communicate in an M2M fashion with a backend server using
GPRS communications. Web and mobile applications have
been developed to allow authorized personnel to access the
data.
Additional techniques under current investigation include
the use of prediction algorithms based on neural networks in
order to estimate pollution information in the near future. In
addition to this prediction in time, prediction in space is also
an interesting research topic: given the pollution levels at the
locations of the monitoring stations, it would be interesting
and challenging to estimate the pollution levels over the whole
area of interest. Another challenging research topic includes