  Many of the devices used in Internet-of-Things (IoT) applications are
energy-limited, and thus supplying energy while maintaining seamless
connectivity for IoT devices is of considerable importance. In this context, we
propose a simultaneous wireless power transfer and information transmission
scheme for IoT devices with support from reconfigurable intelligent surface
(RIS)-aided unmanned aerial vehicle (UAV) communications. In particular, in a
first phase, IoT devices harvest energy from the UAV through wireless power
transfer; and then in a second phase, the UAV collects data from the IoT
devices through information transmission. To characterise the agility of the
UAV, we consider two scenarios: a hovering UAV and a mobile UAV. Aiming at
maximizing the total network sum-rate, we jointly optimize the trajectory of
the UAV, the energy harvesting scheduling of IoT devices, and the phaseshift
matrix of the RIS. We formulate a Markov decision process and propose two deep
reinforcement learning algorithms to solve the optimization problem of
maximizing the total network sum-rate. Numerical results illustrate the
effectiveness of the UAV's flying path optimization and the network's
throughput of our proposed techniques compared with other benchmark schemes.
Given the strict requirements of the RIS and UAV, the significant improvement
in processing time and throughput performance demonstrates that our proposed
scheme is well applicable for practical IoT applications.
