In recent years, the explosive development of the 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 recently emerged new computing paradigms, e.g., Edge Computing and Fog Computing have provided a beneficial complement to the traditional cloud-based systems, as Edge/Fog can provide partial computing resources closer to the user or device side; thereby certain computing tasks can be executed or processed directly by the close Edge/Fog resources without sending them to the distant cloud center, through which a load of a cloud platform can be reduced 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 a 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. Nevertheless, the integration of AI and edge/fog techniques is still a challenging issue that needs intensive study.

The aim of this Special Issue to highlight the cutting-edge research and applications related to the AI powered service optimization for computing. This Issue is looking for significant findings in tackling new security issues that challenge artificial intelligence in the mobile edge computing environment. Specifically, we solicit original research and review articles on secure artificial intelligence from a variety of perspectives (e.g., architecture, data, algorithms, etc).

Potential topics include but are not limited to the following:

• AI-based placement of edge/fog nodes
• Trust, privacy and security issues for computing
• Offloading method design for computing management
• Resource management for edge/fog through big data mining
• Intelligent service discovery and recommendation
• Composition and collaboration of edge/fog services
• Provision, scheduling and maintenance of intelligent services
• Quality of Service (QoS) modeling, measurement and optimization of computing services
• Energy optimization and cost minimization of computing services
• AI-based exception detection of nodes in computing services
• Distributed data integration of Cloud and computing services
• Novel AI-based applications in computing services environment
• Emerging architecture/framework/model for computing