In recent years, the massive deployment of sensors and actuators had made the Industrial Internet of Things (IIoT) highly important for achieving ubiquitous connectivity. The presence of massive industrial networking terminals means the generation of huge amount of industrial data. Industrial big data (IBD), as a product of the combination of Big Data, Internet, and industrial Automation, is a basic strategic resource for the digital, networked, and intelligent development of the manufacturing industry. IBD has a significant and far-reaching impact on the production mode, operation mode and ecological system of manufacturing industry. The existing 5G technology has played an important role in meeting the low delay and high reliability in the IIoT. However, in the complex and highly mobile wireless environment, reliable industrial big data transmission is still a very challenging problem.

Artificial intelligence (AI) has emerged as a promising technology to resolve data processing and mobile transmission problems in IIoT. AI-based applications have shown significant potential to achieve the goals and demands of future networks. Owing to the data-driven approach, AI has brought a paradigm shift to the design and optimization of networks. Consequently, many researchers in IIoT area are focusing on AI for its applications, and AI algorithms are being developed for specific applications.

This Special Issue aims to foster research and innovation in the application of AI for IIoT applications and provides a platform for dissemination of both theoretical and applied results. This is motivated by the requirements of future IIoT networks, in which AI can be a key enabler. Original research and review articles are welcome.

Potential topics include but are not limited to the following:

• AI-powered intelligent Network-layer protocols, frameworks, infrastructures, IoT devices
• AI-based network security and privacy for IIoT networks
• AI techniques for non-linear signal processing in Industrial Big Data Transmission
• AI for IIoT and massive connectivity
• AI-based hybrid learning methods for channel estimation, modeling, prediction, and compression of IIoT networks
• Deep reinforcement learning for radio resource management in IIoT
• AI-enabled techniques for ensured communication in Industrial Big Data Transmission wireless applications
• Edge cloud computing, fog computing, and edge-enabled AI for IIoT applications