  Smart city services are thriving thanks to a wide range of technological
advances, namely 5G communications, Internet of Things (IoT), and artificial
intelligence. Central to this is the wide deployment of smart sensing devices
and accordingly the large amount of harvested information to be processed for
timely decision making. Robust network access is, hence, essential for
offloading the collected data before a set deadline, beyond which the data
loses its value. In environments where direct communication can be impaired by
blockages, unmanned aerial vehicles (UAVs) can be considered as an alternative
for enhancing connectivity, particularly when IoT devices (IoTDs) are
constrained with their resources. Moreover, to conserve energy, IoTDs are
assumed to alternate between their active and passive modes. This paper,
therefore, considers a time-constrained data gathering problem from a network
of sensing devices and with assistance from a UAV. A reconfigurable intelligent
surface (RIS) is deployed to further improve the connectivity to the UAV,
particularly when the multiple devices are served concurrently and experience
different channel impairments. This integrated problem brings challenges
related to the configuration of the phase shift elements of the RIS, the
scheduling of IoTDs transmissions, and the trajectory of the UAV. First, the
problem is formulated with the objective of maximizing the total number of
served IoTDs each during its activation period. Owing to its complexity and the
incomplete knowledge about the environment, we leverage deep reinforcement
learning in our solution; the UAV trajectory planning is modeled as a Markov
Decision Process, and Proximal Policy Optimization is invoked to solve it.
Next, the RIS configuration is then handled via Block Coordinate Descent.
Finally, extensive simulations are conducted to demonstrate the efficiency of
our solution approach.
