Special Issue on Evolution of Networked AI Systems: Trends, Challenges, and Opportunities Submission Date: 2023-12-30 The integration of AI and networking technologies is driving the development of intelligent and autonomous systems that are capable of making decisions and performing tasks in real-time. These systems are critical for a wide range of applications, including 5G networks, IoT, and edge computing. However, designing, implementing, and deploying AI systems in networked environments presents a number of unique challenges. One of the main challenges is ensuring that AI systems can operate effectively in dynamic network environments with varying network conditions, particularly if large-scale changes happen over time. Another requirement is balancing the trade-offs between the computational and communication requirements of AI systems in networked environments. Additionally, there is a need to develop new algorithms and protocols that can effectively utilize the resources of the network to meet the needs of next-generation applications (for example, AR/VR or large language models) while ensuring robustness and security. Finally, networked AI systems will deal with the high-dimensionality and heterogeneity of the data generated by AI systems in networked environments, which requires novel data management and analytics techniques. The goal of this special issue is to invite researchers and practitioners working on the design, implementation, and deployment of AI systems in networked environments. By inviting experts from academia and industry, the special issue aims to foster collaboration and to promote the development of new ideas and research directions in this field. We invite submissions of original research papers, as well as papers describing practical experiences, case studies, and tutorials.


Key topics include (but are not limited to): Distributed AI algorithms and systems, Edge computing and fog computing for AI, AI-enabled networking architectures and protocols, AI-based network management and control, AI-based enhancement for reconfigurable wireless network design, optimization, and resource allocation, Functional decomposition and placement over RAN and edge for AI, Real-time networking protocols for Edge AI, Theoretical and/or experimental results addressing the predictability of networked AI systems from a computational and communication standpoint, Enhanced intelligent network slicing for edge AI platforms, AI-based network troubleshooting and diagnosis, AI-enabled network security and privacy, Data management, sharing, and sets for AI in networked systems, Real-world deployment and evaluation of AI in networked systems, Digital twin platforms enabled by networked AI systems, Large Langue Models (LLMs) in and for networked AI systems.


Guest editors:

Roberto Morabito, PhDUniversity of Helsinki, Helsinki, Finland(networked systems, distributed AI, edge computing, Internet of Things)

Kwang Taik Kim, PhDPurdue University, West Lafayette, Indiana, USA(communication engineering, open RAN architecture, edge platform, large-scale distributed computing)

Kyunghan Lee, PhDSeoul National University, Seoul, Korea(Networked Computing, Mobile Machine Learning, Low-Latency Networking, Data/Computing/Offloading)

Jiasi Chen, PhDUniversity of Michigan, Ann Arbor, USA(mobile systems, AR/VR, video, streaming, machine learning)

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