Integrating Edge Intelligence, Digital Twins, and Blockchain Frameworks for Robust Cyber-Physical Security in Next-Generation Energy and Industrial IoT Networks

Dr. Simone John

Authors

Dr. Simone John Department of Electrical Engineering and Computer Science, University of Melbourne, Australia

Keywords:

Digital Twin, Edge Intelligence, Industrial IoT, Blockchain Security

Abstract

The rapid convergence of the Industrial Internet of Things (IIoT), Digital Twin (DT) technology, and decentralized communication protocols has ushered in a new era of smart manufacturing and energy management. However, this integration introduces unprecedented security vulnerabilities across multilayered network architectures. This research article investigates the synchronization of Edge Intelligence (EI) and Digital Twin frameworks to enhance predictive diagnostics and operational resilience in energy networks. By leveraging machine learning algorithms for the identification and classification of cyberattacks within Internet of Blockchain (IoBc) environments, this study proposes a decentralized security paradigm. We examine model compression techniques as a necessity for deploying complex intelligence at the edge and evaluate the role of semantic communication in visual data transmission for autonomous systems, such as Unmanned Aerial Vehicles (UAVs). Through a comprehensive analysis of digital twin-driven shop floors and smart grid-powered wireless networks, this paper identifies critical research gaps in cross-domain standardization and trust management. Our findings suggest that a cognitive adaptive system, supported by reinforcement learning and high-fidelity physical simulations, provides a robust defense against adversarial threats while maintaining high energy efficiency in 6G-enabled industrial ecosystems.

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