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

TL;DR

This paper proposes a UAV-assisted digital twin framework for IoT networks that enhances synchronization and accuracy using a novel AoDT metric, optimizing data collection and real-time system monitoring.

Contribution

It introduces a high-fidelity digital twin framework with a new AoDT metric and an optimization approach for UAV placement and data collection to improve IoT system synchronization.

Findings

The proposed framework achieves high accuracy and synchronization in digital twins.

Optimization of UAV locations improves data freshness and system monitoring.

Simulation results outperform baseline methods in data collection efficiency.

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.