Submission Deadline: 01 July 2025 View: 99 Submit to Special Issue
Prof. Chao-Yang Lee
Email: chaoyang@yuntech.edu.tw
Affiliation: Department of Computer Science and Information Engineering, National Yunlin University of Science,Douliou 640301, Taiwan
Research Interests: non-terrestrial networks, artificial intelligence (AI), AI-powered drones, intelligent connected and autonomous vehicles, and pattern recognition
Prof. Ang-Hsun Tsai
Email: ahtsai@fcu.edu.tw
Affiliation: Department of Communications Engineering, Feng Chia University, 407032,Taichung, Taiwan
Research Interests: 6G Mobile Communication Network, Non-Terrestrial Communication Networks, Wireless Communication System, Aerial Communication Network, Disaster-Resilient Communication Network, Radio Resource Management, LEO Multibeam Satellite Communication Networks
This Special Issue delves into the convergence of AI-enhanced Edge and Fog Computing. As both Edge and Fog Computing continue to advance at a rapid pace, there is an increasing need to understand their synergistic potential and explore a range of innovative applications. Moreover, with significant progress in artificial intelligence (e.g., deep learning), AI-driven computing techniques (such as deep learning-based applications) are achieving state-of-the-art results across various fields. This Special Issue aims to showcase pioneering research and applications that illustrate the integration of AI-augmented Edge and Fog Computing for resilient and high-performance solutions.
We welcome researchers to submit original research papers, reviews, and case studies on topics including, but not limited to:
· Development and optimization of AI-augmented Edge and Fog Computing;
· Real-time implementation and hardware acceleration for AI-augmented Edge and Fog Computing;
· Applications of AI-augmented Edge and Fog Computing in fields such as autonomous vehicles, surveillance, robotics, and smart environments;
· Novel sensor technologies and their impact on AI-augmented Edge and Fog Computing;
· Deep learning methodologies for AI-augmented Edge and Fog Computing.