Guest Editors
Dr. Sang-Bing Tsai, Wuyi University
Dr. Yuan Yuan, Michigan State University
Dr. Xuyun Zhang, Macquarie University
Summary
Edge computing (EC) is designed to provide edge intelligence services near the edge of the network or near the data source. Massive data no longer must be transmitted to the distant cloud platforms, and it can be solved on the edge side, which is more suitable for the key needs of industry digitalization in agile connection, real-time business, data optimization, application intelligence, security and privacy protection.
As there are massive and heterogeneous data in the edge terminal, it is necessary to integrate the advantageous artificial intelligence (AI) techniques into EC, and put most of the data in the edge closer to the data source for intelligent processing, to improve efficiency and relieve the load pressure of the platform. At the same time, implementing the intelligent tasks directly at the edge terminal can effectively reduce the bandwidth requirement, provide timely response, and realize protection for data privacy of the edge terminals. Besides, the introduction of AI in EC can not only perform business logic analysis and calculation autonomously, but also dynamically and self-optimize and adjust execution strategies for IoT applications in real time.
This special issue focuses on the fundamental strategies, theories, algorithms and applications in Artificial Intelligence and Edge Computing for industrial applications. It aims to share and discuss recent advances and future trends to bring academic researchers and industry developers together.
Keywords
• Emerging architecture/framework/models for the collaboration of EC and CC in IoT with AI
• AI-based placement of edge nodes for IoT applications
• Cost-efficient resource management for EC through big data mining
• Service quality evaluation in EC platforms
• Privacy/security/trust issues for EC-based IoT applications
• Novel applications based on AI for EC in IoT
• Energy-aware offloading methods for EC management with AI
• Management and Optimization of Multi-source data fusion in IoT
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