The fast expansion of the Internet of Things (IoT) has led to the Industrial IoT (IIoT). In the near future, more than 50 billion devices are expected to be connected to the internet, with around 20 percent of these coming from the industry sector. The technological evolution may optimize operational efficiency, automation, maintenance, and rationalization. Moreover, it ensures large-scale interaction between machines, computers, and people, enabling advanced industrial systems which are capable of autonomous decision making, self-diagnosis, and environment adaptation.

Edge computing, which processes data at the nearest available node, has been proposed as a feasible solution for efficient collection and processing of data in intelligent industrial systems. Edge devices can immediately respond to changes in the environment, which helps improve the adaptation of industrial systems. As a distributed system, smart edge nodes are capable of making local optimal decisions, operating normally without central control in a period of time, automatically cooperating with each other for complex tasks.

The objective of this Special Issue is to solicit high-quality work regarding recent advances in solving the aforementioned smart edge issues and related developing technologies in intelligent industrial systems. We welcome both original research and review articles.

Potential topics include but are not limited to the following:

• IIoT networking framework for deep learning
• Fault tolerance in IIoT systems
• Algorithms, schemes, and techniques in IIoT systems for deep learning
• Parallel computing in IIoT systems for deep learning
• Optimization and distributed control in IIoT systems
• Distributed infrastructures and parallelization in IIoT systems
• Resource allocation and scheduling in IIoT systems
• Security and privacy in IIoT systems