Digital Twin Technology in Industrial IoT Environments

Authors

  • Muhammad Asad Sardar National University of Sciences and Technology (NUST), Islamabad, Pakistan
  • Erum Amir National University of Sciences and Technology (NUST), Islamabad, Pakistan
  • Malik Abdul Rehman National University of Sciences and Technology (NUST), Islamabad, Pakistan

Keywords:

Digital Twin, Predictive Maintenance, Real-Time Simulation, Operational Efficiency, Artificial intelligence, Edge Computing, Data-Driven Decision Making

Abstract

Digital Twin (DT) technology has emerged as a transformative force in Industrial Internet of Things (IIoT) environments, enabling real-time simulation and optimization of physical assets. This paper explores the integration of DTs within IIoT frameworks, emphasizing their role in predictive maintenance, operational efficiency, and data-driven decision-making. By examining case studies and current implementations, the study highlights the benefits and challenges associated with DT adoption in industrial settings. The research also discusses future directions, including the incorporation of artificial intelligence and edge computing to enhance DT capabilities.

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Published

2023-12-31

Issue

Jul-Dec 2023

Section

Articles

License

Copyright (c) 2023 International journal of advanced sciences and computing

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