Guest Editors
Prof. Chase Wu
Email: chase.wu@njit.edu
Affiliation: Department of Data Science, New Jersey Institute of Technology, Newark, NJ, 07102, USA
Homepage:
Research Interests: big data, machine learning, high-performance networking, parallel and distributed computing, sensor networks, scientific visualization, and cyber security
Prof. Jaime Lloret
Email: jlloret@dcom.upv.es
Affiliation: Department of Computer Engineering, Universitat Politècnica de València, Valencia, 46022, Spain
Homepage:
Research Interests: designing and developing network protocols and algorithms, especially on group-based topologies.
Prof. Songlin He
Email: sohe@swjtu.edu.cn
Affiliation: School of Computing and Artificial Intelligence, Southwest Jiaotong University, Chengdu, 611756, China
Homepage:
Research Interests: blockchain, IoT, cybersecurity, big data, machine learning
Summary
Industrial enterprises are undergoing a transformative shift toward digitization, intelligence, and comprehensive interconnection. This revolution is being accelerated by rapid advancements in artificial intelligence, distributed computing, and security technologies such as cryptography and privacy-preserving computing. These developments have made it possible to collect and analyze unprecedented volumes of data across the entire industrial lifecycle—from process planning and scheduling, machinery status monitoring, and device control, to the operation of mechanical and electrical equipment. Furthermore, this digital evolution extends throughout the full business workflow, encompassing industrial chain integration, dataspace construction, transactional processes, after-sales services, supply-demand coordination, and value chain optimization. In many industrial environments, data acquisition and status monitoring are increasingly enhanced through the deployment of unmanned aerial vehicles (UAVs) and robotic systems.
The massive scale, volume, and complexity of industrial data have created a strong foundation for developing data-driven solutions powered by machine learning. At the same time, they present significant challenges in data storage, management, processing, and analysis—challenges that must be addressed to achieve performance optimization, reduce operational costs, and generate actionable business intelligence. This special issue aims to explore recent advances in theories, methodologies, technologies, and practical applications related to industrial interconnection, intelligence, and optimization. Topics of interest include, but are not limited to, enterprise resource abstraction, industrial identity systems, industrial interconnection communication, fault detection and localization, device control, demand forecasting, resource allocation, decision support, and value chain management and control. Particular emphasis is placed on the use of artificial intelligence and machine learning across diverse industrial settings and business domains. This issue also serves as a platform for academic researchers, industry professionals, and application practitioners to exchange innovative ideas, share real-world experiences, and collaboratively address the complex challenges emerging in this rapidly evolving field.
This special issue solicits high-quality, original papers reporting research or experimental results on the following topics, but not limited to:
· Industrial enterprise resource abstraction
· Industrial interconnection and identity
· Industrial IoT and operating systems
· Industrial sensors and actuators
· Industrial chain and supply chain collaboration and optimization
· Intelligent services for industrial chain and supply chain
· Security for industrial chain and supply chain
· Large language models for industrial chain
· Model context protocols for industrial chain
· Data security and privacy using blockchain
· Big data technology for industrial processes
· Fault detection and localization
· Device control and optimization
· Energy-efficient manufacturing
· Demand prediction and resource allocation
· End-edge-cloud collaboration for industrial automation and optimization
· Artificial intelligence and machine learning for industrial applications
Keywords
Artificial intelligence, machine learning, big data, industrial systems
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