Advances in High-Efficiency AI-Enabled Edge Computing for Distributed Networking Systems
A special issue of Electronics (ISSN 2079-9292).
Deadline for manuscript submissions: 15 October 2025 | Viewed by 432
Special Issue Editors
Interests: machine learning; computer networking; distributed computing; blockchain
Interests: data mining; machine learning
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
The Special Issue, "Advances in High-Efficiency AI-Enabled Edge Computing for Distributed Networking Systems", focuses on emerging research and technological breakthroughs integrating Artificial Intelligence (AI) techniques with edge computing paradigms. Its primary objective is to address challenges and opportunities associated with processing massive datasets at network edges, thereby improving efficiency, responsiveness, and scalability within distributed networking systems.
This Special Issue consolidates recent advances, facilitates knowledge exchange among researchers, and provides a comprehensive resource that can drive future research directions at the intersection of AI and edge computing. It aims to inspire innovative approaches and foster collaborations to address critical performance bottlenecks and enhance the operational capabilities of distributed networking systems.
This Special Issue complements existing research by bridging the gap between theoretical research and practical applications. While current publications have extensively discussed AI and edge computing separately, there remains limited exploration specifically targeted at their synergistic integration within distributed networks. Thus, the Special Issue addresses this gap by offering novel insights, methodologies, and empirical results, enhancing the academic and industrial understanding of high-efficiency AI-enabled edge computing.
The topics of interest for this Special Issue include, but are not limited to, the following:
- Novel AI algorithms that are specifically optimized for edge environments.
- Efficient edge computing architectures and frameworks incorporating AI.
- Practical implementation of AI-based solutions for real-time analytics and decision making.
- AI-driven resource management and allocation in distributed edge networks.
- Security, privacy, and reliability enhancements enabled by AI in edge computing contexts.
- Case studies that highlight significant advancements and deployment outcomes.
- AI-enabled edge computing in 5G networks and beyond.
- Performance evaluation and benchmarking of edge computing systems.
- Surveys for the essence of edge computing from AI perspectives.
Dr. Dajun Zhang
Dr. Xiaowei Jia
Prof. Dr. F. Richard Yu
Guest Editors
Manuscript Submission Information
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Keywords
- edge computing
- AI
- distributed networks
- security
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