Investigation and Empirical Analysis of Transfer Learning for Industrial IoT Networks

The Industrial Internet of Things Networks (IIoT-N) have revolutionized industrial systems by connecting sensors, devices, and data analytics, creating complex, data-driven environments. However, key challenges persist, such as data diversity, scalability issues, sparse data, anomaly detection, and adapting to changing conditions while managing limited resources. To address these, Transfer Learning, a machine learning technique, has become a practical solution, enhancing IIoT-N by enabling better data integration, real-time analytics, improved fault detection, and adaptable models with minimal retraining, all while optimizing resource usage. This paper examines the role of Transfer Learning in IIoT-N, identifying research gaps and challenges, and highlights its potential across various industrial applications, including predictive maintenance, anomaly detection, edge computing, process optimization, and cross-domain knowledge transfer. Special attention is given to manufacturing, where Transfer Learning shows significant promise. The study also proposes a taxonomy of approaches for integrating Transfer Learning into IIoT-N. These include Domain Adaptation Techniques, Data Augmentation and Synthesis, Hybrid Models using Ensemble Learning, and practical strategies for successful implementation. Additionally, a detailed case study demonstrates the real-world application of Transfer Learning in industrial networks, showcasing its practical benefits. Thus, this paper contributes to the advancement of Transfer Learning in IIoT-N by providing insights into its potential to enhance industrial processes and address existing challenges. It offers theoretical and practical perspectives on how Transfer Learning can be effectively applied in industrial environments, driving efficiency and adaptability.