  The massive growth of Smart City and Internet of Things applications enables
safety and security. The data those are produced from surveillance cameras in
aerial devices such as unmanned aerial networks (UAVs) are needed to be
transferred to ground stations for secure data analysis. When the scale of
network is relatively large compare to the wireless communication coverage of
device, it is not always available to transmit the data to the ground stations,
thus distributed and autonomous algorithms are essentially desired. Based on
the needs, we propose a novel algorithm that is for collecting surveillance
data under the consideration of mobility and flexibility of UAV networks. Due
to the battery limitation in UAVs, we selectively collect data from the UAVs by
setting rules under the consideration of distance and similarity. As a
sequence, the UAV devices have to compete for a chance to get data processing.
For this purpose, this paper designs a Myerson auction-based deep learning
algorithm to leverage the UAV's revenue compare to traditional second-price
auction while preserving truthfulness. Based on simulation results, we verify
that our proposed algorithm achieves desired performance improvements.
