Changhui Bae , Euteum Choi , Sungjoo Kang , Sungsoo Ahn , Seongjin Lee
{"title":"Digital twin platform for real-time data communication in UAV environment","authors":"Changhui Bae , Euteum Choi , Sungjoo Kang , Sungsoo Ahn , Seongjin Lee","doi":"10.1016/j.future.2025.108078","DOIUrl":null,"url":null,"abstract":"<div><div>Real-time data communication is essential for controlling objects from virtual space to physical space through digital twins. However, existing digital twin platforms for UAV environments primarily focus on data modeling, prediction, and simulation rather than real-time performance and have not been extensively evaluated for real-time data communication, which may limit their applicability in real-world UAV operations. This paper introduces the RC-DT(Real-Time Communication Digital Twin)-Platform, which supports real-time data communication in UAV environments. The RC-DT-Platform’s data communication performance was evaluated by measuring the throughput as bandwidth, and the number of registered items increased. Results show that the RC-DT-Platform can transmit approximately 454 data/sec for 100 bytes data, 119 data/sec for 100 KB data, and 0.7 data/sec for 16 MB of data. Additionally, with 32 registered objects, the RC-DT-Platform can achieve a read throughput of about 3500 data/sec, regardless of data size. The performance of pure Ditto degrades by up to approximately 10 times as the number of registered objects increases up to 32, whereas the RC-DT-Platform maintains a degradation of less than 6.25 times. Thus, the RC-DT-Platform meets the required real-time data communication performance by considering flight speed, data size, and data generation rate.</div></div>","PeriodicalId":55132,"journal":{"name":"Future Generation Computer Systems-The International Journal of Escience","volume":"175 ","pages":"Article 108078"},"PeriodicalIF":6.2000,"publicationDate":"2025-08-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Future Generation Computer Systems-The International Journal of Escience","FirstCategoryId":"94","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0167739X25003723","RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"COMPUTER SCIENCE, THEORY & METHODS","Score":null,"Total":0}
引用次数: 0
Abstract
Real-time data communication is essential for controlling objects from virtual space to physical space through digital twins. However, existing digital twin platforms for UAV environments primarily focus on data modeling, prediction, and simulation rather than real-time performance and have not been extensively evaluated for real-time data communication, which may limit their applicability in real-world UAV operations. This paper introduces the RC-DT(Real-Time Communication Digital Twin)-Platform, which supports real-time data communication in UAV environments. The RC-DT-Platform’s data communication performance was evaluated by measuring the throughput as bandwidth, and the number of registered items increased. Results show that the RC-DT-Platform can transmit approximately 454 data/sec for 100 bytes data, 119 data/sec for 100 KB data, and 0.7 data/sec for 16 MB of data. Additionally, with 32 registered objects, the RC-DT-Platform can achieve a read throughput of about 3500 data/sec, regardless of data size. The performance of pure Ditto degrades by up to approximately 10 times as the number of registered objects increases up to 32, whereas the RC-DT-Platform maintains a degradation of less than 6.25 times. Thus, the RC-DT-Platform meets the required real-time data communication performance by considering flight speed, data size, and data generation rate.
期刊介绍:
Computing infrastructures and systems are constantly evolving, resulting in increasingly complex and collaborative scientific applications. To cope with these advancements, there is a growing need for collaborative tools that can effectively map, control, and execute these applications.
Furthermore, with the explosion of Big Data, there is a requirement for innovative methods and infrastructures to collect, analyze, and derive meaningful insights from the vast amount of data generated. This necessitates the integration of computational and storage capabilities, databases, sensors, and human collaboration.
Future Generation Computer Systems aims to pioneer advancements in distributed systems, collaborative environments, high-performance computing, and Big Data analytics. It strives to stay at the forefront of developments in grids, clouds, and the Internet of Things (IoT) to effectively address the challenges posed by these wide-area, fully distributed sensing and computing systems.