In recent years, the explosive development of Internet of Things (IoT) has generated a large amount of data from both the user-device side and the network side, which challenges the traditional cloud-based data transmission, storage and processing applications in terms of efficiency, security and economic cost. In this situation, the newly emerged computing paradigms, Edge Computing and Fog Computing, have complemented the traditional cloud-based systems. Edge/Fog can provide partial computing resources closer to the user or device side; certain computing tasks can thereby be executed or processed directly by the close Edge/Fog resources without sending them to the distant cloud centre. This can reduce the load of a cloud platform and the task execution efficiency can be improved significantly.

However, existing Edge/Fog-based service systems often suffer from limited computing capabilities, high energy cost, and fast changing context environment, which call for intelligent optimization of business strategies adopted in both the user-device side and network side. Fortunately, artificial intelligence (AI) technology provides a promising way to achieve the above Edge/Fog service optimization goals. However, the integration of AI and Edge/Fog techniques is still a challenging issue that needs intensive study. In view of the above analyses, this Special Issue aims to highlight the cutting-edge research and applications related to the “AI Powered Service Optimization for Edge/Fog Computing”.

In this Special Issue, we look for significant findings in tackling new security issues that challenge artificial intelligence in the mobile edge computing environment. Specifically, we solicit novel contributions on secure artificial intelligence from a variety of perspectives, e.g., architecture, data, algorithms, etc. Both original research and review articles are welcomed.

Potential topics include but are not limited to the following:

• Trust, privacy, and security issues for Edge/Fog based on AI
• Offloading method design for Edge/Fog management
• Resource management for Edge/Fog through big data mining
• Intelligent service discovery and recommendation in Edge/Fog
• Composition and collaboration of Edge/Fog services with AI
• Provision, scheduling, and maintenance of intelligent services
• QoS modelling, measurement, and optimization of Edge/Fog services
• Energy optimization and cost minimization of Edge/Fog services
• Distributed data integration of Cloud and Edge/Fog
• Novel applications in Edge/Fog environment
• Emerging architecture/framework/models