A UAV-Aided Digital Twin Framework for IoT Networks With High Accuracy and Synchronization

Digital Twin (DT) technology has emerged as a promising link between the physical and virtual worlds, enabling simulation, prediction, and real-time performance optimization in different domains. In this work we develop a high-fidelity digital twin framework, focusing on synchronization and accuracy between physical and digital systems to enhance data-driven decision making. To achieve this, we deploy several stationary UAVs in optimized locations to collect data from IoT devices, which were used to monitor multiple physical entities and perform computations to evaluate their status. We formulate a mixed-integer non-convex program to maximize the total amount of data collected from all IoT devices while ensuring a constrained age of digital twin threshold and solve it using successive convex approximation (SCA). To cope with realistic scenarios involving unpredictable environments and large network sizes, we model our problem as a Markov Decision Process (MDP), and propose a deep reinforcement learning-based approach using a Twin Delayed Deep Deterministic Policy Gradient (TD3) to optimize the unmanned aerial vehicle positions and the sum rate. Finally, we present different simulation results of the SCA and TD3 based solutions together with two baseline approaches and evaluated the sum rate in terms of IoT device count, AoDT threshold, task arrival rate and UAVs’ computational capacity. In all simulation results, the proposed TD3-based approach consistently proved to be superior as compared to the baseline solutions.