Title: 5G-powered digital twin for orchestration and prediction task based on cascaded LSTM

Authors: Sridharan Kannan; Ahmad Y.A. Bani Ahmad; Thella Preethi Priyanka; Yogapriya J.

Addresses: Department of Artificial Intelligence and Data Science, J.K.K. Munirajah College of Technology, T.N.Palayam, Gobichettipalayam, Erode, 638506, Tamil Nadu, India ' Faculty of Business, Department of Accounting and Finance, Middle East University, Amman, 11831, Jordan ' Department of Computer Science Engineering, Koneru Lakshmaiah Educational Foundation, Vaddeswaram, Guntur, Andhra Pradesh 522502, India ' Department of Computer Science and Engineering, Saveetha School of Engineering, Saveetha Institute of Medical and Technical Sciences, Thandalam, 602105, Chennai, Tamilnadu, India

Abstract: An advanced deep learning-based 5G-powered DT replica for orchestration and monitoring purposes is implemented in this paper. The major aim of this paper is the development of the MNDT replication, which is capable of being implemented in a 5G core environment. This model is found helpful in various advanced applications like Industry 4.0 and cyber security. The required model for the development of the MNDT is gathered by utilising a data acquisition approach. Further, the modelling is performed by making use of bidirectional connections within the digital and physical elements. Once the data collection is complete, and the MNDT is developed, then the second phase of the work is executed. Here, the prediction is carried out using the cascaded long short-term memory (CasLSTM) framework. Experimental validations are carried out to prove the efficacy of the implemented deep learning-based 5G-powered MNDT for the prediction model.

Keywords: 5G technology; digital twin; CasLSTM; cascaded long short term memory; data acquisition; orchestration and prediction task.

DOI: 10.1504/IJAACS.2025.149809

International Journal of Autonomous and Adaptive Communications Systems, 2025 Vol.18 No.5, pp.463 - 484

Received: 27 Dec 2024
Accepted: 10 Feb 2025
Published online: 13 Nov 2025 *

Full-text access for editors Full-text access for subscribers Purchase this article Comment on this article