A UAV-Aided Digital Twin Framework for IoT Networks with High Accuracy and Synchronization
Ghofran Khalaf, May Itani, Sanaa Sharafeddine
TL;DR
The paper tackles the challenge of achieving high-fidelity digital twins for industrial IoT by coupling data collection and real-time synchronization through UAVs. It formalizes a mixed-integer non-convex program to maximize data throughput while constraining the AoDT, solved via successive convex approximations within a UAV placement loop, and derives a closed-form AoDT for multi-IoT-per-entity monitoring. A LCFS-S queuing strategy is adopted to minimize age, and the approach is validated through simulations showing substantial gains in data freshness and DT accuracy compared to baselines (e.g., up to $8.8$ Mbps with five UAVs and $12$ Mbps under heavier device counts). The results demonstrate the practical impact of AoDT-aware optimization for reliable, synchronized DTs in industrial IoT environments, enabling faster, data-driven decisions and improved system resilience.
Abstract
With the continued growth of its core technologies, including the Internet of Things (IoT), artificial intelligence (AI), Big Data and data analytics, and edge computing, digital twin (DT) technology has witnessed a significant increase in industrial applications, helping the industry become more sustainable, smart, and adaptable. Hence, DT technology has emerged as a promising link between the physical and virtual worlds, enabling simulation, prediction, and real-time performance optimization. This work aims to explore the development of 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 industrial IoT devices, which were used to monitor multiple physical entities and perform computations to evaluate their status. We consider a practical setup in which multiple IoT devices may monitor a single physical entity, and as a result, the measurements are combined and processed together to determine the status of the physical entity. The resulting status updates are subsequently uploaded from the UAVs to the base station, where the DT resides. In this work, we consider a novel metric based on the Age of Information (AoI), coined as the Age of Digital Twin (AoDT), to reflect the status freshness of the digital twin. Factoring AoDT in the problem formulation ensures that the DT reliably mirrors the physical system with high accuracy and synchronization. We formulate a mixed-integer non-convex program to maximize the total amount of data collected from all IoT devices while ensuring a constrained AoDT. Using successive convex approximations, we solve the problem, conduct extensive simulations and compare the results with baseline approaches to demonstrate the effectiveness of the proposed solution.