  Intelligent Transportation Systems are thriving thanks to a wide range of
technological advances, namely 5G communications, Internet of Things,
artificial intelligence and edge computing. 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, for instance, blockages such as in
urban cities, unmanned aerial vehicles (UAVs) can be considered as an
alternative for providing and enhancing connectivity, particularly when IoT
devices (IoTD) are constrained with their resources. Also, 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 both the connectivity
and energy efficiency of the UAV, particularly when 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 as well as the
trajectory of the UAV. First, the problem is formulated with the objective of
maximizing the total number of served devices 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.
