Organizers
Chair (main contact person): Weijun Wang（王维军）, Jimei University,
lecturer, No. 185 Yinjiang Road, Jimei District, Xiamen
Co-chair: Shenhua Yang（杨神化）, Jimei University, professor, No.
185 Yinjiang Road, Jimei District, Xiamen
Co-chair: Yongfeng Suo（索永峰）, Jimei University, professor, No.
185 Yinjiang Road, Jimei District, Xiamen
Co-chair: Guoquan Chen（陈国权）, Jimei University, associate
professor, No. 185 Yinjiang Road, Jimei District, Xiamen
Co-chair: Dan Song（宋丹）, Jimei University, associate professor,
No. 185 Yinjiang Road, Jimei District, Xiamen
Co-chair: Qiong Chen（陈琼）, Jimei University, lecturer, No. 185
Yinjiang Road, Jimei District, Xiamen
Abstract
In recent years, edge computing and intelligent clusters have garnered widespread attention in intelligent systems. With the rapid development of the Internet of Things (IoT), 5G/6G, and autonomous systems, traditional cloud computing faces increasing challenges related to latency, bandwidth, and scalability. Edge computing provides computing power closer to the data source, thereby improving response time and system efficiency. Intelligent clusters, through the collaboration of autonomous intelligent agents, enhance the application potential of edge computing, though they still face numerous challenges.
The goal of this direction is to explore the integration of edge computing and intelligent clusters, focusing on new networking technologies, cluster collaboration, task planning, and the application of large language models in task allocation and planning. It also aims to investigate the potential of deep reinforcement learning in optimizing cluster decision-making and enhancing system intelligence. This track will analyze the application prospects of these technologies in autonomous driving, cross-domain collaboration across land, sea, and air, industrial automation, and other fields, while delving into the technical challenges and future development directions.
This track focuses on the latest advancements in edge computing and intelligent systems, with an emphasis on novel networking technologies, deep learning, multi-agent collaboration, and cross-domain applications. As the demand for real-time decision-making, resource optimization, and intelligent coordination in distributed systems grows, edge computing combined with emerging technologies such as deep reinforcement learning, federated learning, and large language models plays a pivotal role in enabling autonomous systems and collaborative networks.
We seek original, completed, and unpublished work not currently under review by any other journal/magazine/conference. Topics of interest include, but are not limited to:
New networking technologies for edge computing in intelligent clusters
Edge intelligence and deep learning
Multi-agent systems and deep reinforcement learning
Federated learning
Large language model-based task allocation and planning in distributed systems
Integration of edge computing with 5G/6G networks and autonomous systems
Cross-domain collaboration across land, sea, and air technologies
Intelligent ships and remote control