RIS-Assisted UAV for Timely Data Collection in IoT Networks

Smart city services 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. 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 help 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 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 (MDP), and Proximal Policy Optimization (PPO) is invoked to solve it. Next, the RIS configuration is then handled via Block Coordinate Descent (BCD). Finally, extensive simulations are conducted to demonstrate the efficiency of our solution approach that, in many cases, outperforms other methods by more than 50%. In addition, simulation results highlight the impact of RIS in significantly improving the UAV energy efficiency. Keywords— IoT Networks, Reconfigurable Intelligent Surface, Deep Reinforcement Learning, Energy Efficiency.