Integration of Edge AI and Digital Twins in Industrial Systems: A Review of Trends and Architectures

Abstract

The amalgamation of the Edge Artificial Intelligence (Edge AI) and Digital Twin (DT) technologies is changing the industrial systems by facilitating faster intelligence, real-time monitoring, and more dependable decision-making. The edge AI performs local processing of data with low latency, whereas digital twins generate virtual models of machines and processes to assist in the analysis, prediction, and optimization. They both improve the efficiency and reliability of operations and automation in Industry 4.0 and IIoT. The current review intends to draw attention to modern trends, architectures, and applications, which combine Edge AI with digital twin systems in manufacturing, energy, logistics, and smart infrastructure. Some of the main research trends are on how to enhance scalability, security, interoperability and data management in interrelated industrial ecosystems. Notwithstanding this, there are still challenges which include fragmented architectures, lack of resources, privacy issues and lack of common standards to large scale deployment. It should focus on lightweight AI models and standard frameworks and federated methods of learning in future work. Comprehensively, this review offers a brief insight into the ability of Edge AI and Digital Twins to work together to create more intelligent, autonomous, and sustainable industrial systems.