As a distributed service architecture, edge computing has supported effectively the development of many different sectors and has received extensive attention from researchers across different industries. People believe that edge computing will contribute a great deal to the construction of future communication networks, however, with the continuous advancement of network technology, the number of network terminal users and data traffic has grown significantly. The computing and network resources of edge nodes are limited and cannot meet the needs of high-density and high-speed tasks. Moreover, the execution of computing tasks on the edge is vulnerable to threats of malicious behavior, which severely hinders the large-scale deployment of edge computing programs.

Artificial intelligence (AI) technology is expected to overcome the above-mentioned challenges faced by edge computing, thus giving birth to a new research direction called intelligent edge computing (IEC). For example, deep reinforcement learning (DRL) can be used for resource allocation in edge computing networks, supervised learning can detect security threats to communication networks, and distributed machine learning (ML) can coordinate communication between different edge network domains. IEC supports more nodes to load traffic and increase the communication rate. At the same time, IEC is closer to users, and data transmission is more secure. Therefore, IEC can provide new solutions to many problems in next generation communication networks. To make IEC better serve future communication networks, new theories, technologies, architectures, algorithms, and mechanisms need to be proposed.

This Special Issue invites researchers from industry and academia to share their latest discoveries and developments in the fields of AI, computing networks, and future communication networks. We welcome both original research and review articles.

Potential topics include but are not limited to the following:

• Design of IEC architecture for future communication networks
• 5G, beyond 5G, or 6G supported by IEC
• Edge computing network resource allocation and management based on AI
• Security and privacy of IEC
• Intelligent edge cache and computing offload
• Mobility management of edge computing devices
• Distributed or collaborative AI for future communication networks
• Edge network resource management for differentiated quality of service or quality of experience
• Joint optimization of computing, network, and storage resources of IEC
• Cross-edge computing network domain resource management and communication protocol design and optimization
• Energy management of IEC devices
• The combination of blockchain, big data, Internet of Things, and smart grid and edge computing