Collision-Free 3-D Navigation of a UAV Team for Optimal Data Collection in Internet-of-Things Networks With Reconfigurable Intelligent Surfaces

This article considers a time-constrained data collection problem from a network of ground sensors mounted on a nonflat terrain by several unmanned aerial vehicles (UAVs). On an uneven terrain, the line-of-sight (LoS) from the sensors to the UAVs is often occluded. Ground-based reconfigurable intelligent surfaces (RISs) are used to make data collection more effective. Data are transmitted to the UAVs either directly or through the RISs. A model-predictive-control-based algorithm for UAVs navigation and data transmission is developed. The algorithm fully addresses the issue of an uneven terrain. It maximizes the amount of data sent to the UAVs within relatively short times and minimizes the energy expenditure of all the UAVs by establishing LoS connection directly or via RISs as often as possible. We present a mathematically rigorous proof of the optimality of the proposed method.