  Traditional terrestrial communication infrastructures often fail to collect
the timely information from Internet of Thing (IoT) devices in remote areas. To
address this challenge, we investigate a Satellite-unmanned aerial vehicles
(UAV) integrated Non-terrestrial network (NTN), where the UAV is controlled by
remote control center via UAV-to-Satellite connections. To maximize the energy
efficiency (EE) of the UAV, we optimize the UAV trajectory, power allocation,
and state sensing strategies, while guaranteing the control stability and
communication reliability. This challenging problem is addressed using an
efficient algorithm, incorporating a Deep Q-Network (DQN)-based trajectory
determination, a closed form of power allocation, and one-dimensional searching
for sensing. Numerical simulations are conducted to validate the effectiveness
of our approach. The results showcase the data size of collection has a greater
impact than transmission power, and reveal the relationship among sensing
interval, communication maximum power and control performance. This study
provides promising solutions and valuable insights for efficient data
collection in remote IoT.
