Organizers 组织者:

• • Peng Yu (Associate Professor), Beijing University of Posts and Telecommunications
  喻鹏，北京邮电大学 未来学院副院长/副教授
• • Michel Kadoch (Professor), École de Technologie Supérieure
  Michel Kadoch, 加拿大魁北克高等技术学院，教授
• • Lei Yu (Associate Professor), Inner Mongolia University
  于磊，内蒙古大学，副教授

Introduction 论坛简介:

Intelligent Network Computing (INC) is rapidly transforming network infrastructures through the convergence of artificial intelligence (AI), big data, and cloud computing. This synergy enables self-learning, adaptive networks capable of managing complex automation tasks. Key advancements include AI-enhanced network management, machine learning for traffic engineering, and resource optimization in edge computing environments. INC’s innovations significantly improve IoT efficiency by enabling massive data exchange across connected devices, while big data analytics deliver deeper operational insights to support data-driven decision making and personalized services. This session brings together academia and industry experts to explore cutting-edge INC developments. Discussions will focus on intelligent network infrastructure, real-time adaptive systems, and next-generation intelligent services, fostering collaboration for sustainable and secure smart networking solutions.

本论坛聚焦智能网络计算（INC）领域的最新突破，探讨人工智能、大数据与云计算驱动的自学习、自适应网络技术。核心议题包括AI赋能的网络管理、边缘计算资源优化、物联网高效数据交换及智能服务创新。论坛旨在汇聚学术界与工业界专家，分享INC在提升网络自动化、实时决策与用户定制化服务方面的前沿成果，推动智能网络基础设施的产学研融合。

Topics 主题范围:

• Data driven intelligence supported approaches and technologies
  数据驱动智能的方法与技术
• Quality of Service (QoS) and Quality of Experience (QoE) support
  服务质量与体验质量优化
• Advanced AI-driven trends for autonomous communication networks
  自组织网络的AI前沿技术
• Intelligent communications and networking for computing
  面向计算的智能通信与网络
• Network fault detection and self-healing
  网络故障检测与自愈
• Network self-configuration and self-organization
  网络自配置与自组织
• Next generation network architecture for intelligent computing
  面向智能计算的下一代网络架构
• AI-based edge computing
  基于 AI 的边缘计算
• Machine learning paradigms for intelligent traffic engineering
  智能流量工程的机器学习范式
• Data-driven decision making: AI models and frameworks in INC
  数据驱动决策：INC 中的 AI 模型与框架
• Intelligent services and user-centric networking in INC
  INC 中的智能服务与以用户为中心的网络
• Automated Machine Learning (AutoML) for zero-touch service and network management
  用于零接触服务与网络管理的自动化机器学习
• Adaptive machine learning techniques for real-time network management
  用于实时网络管理的自适应机器学习技术
• Adaptive machine learning for fast evolving network and communications
  用于快速演进网络与通信的自适应机器学习
• Bayesian optimization for self-healing networks
  用于自愈网络的贝叶斯优化
• Intelligent data-driven approaches for data fusion in 6G
  用于 6G 数据融合的智能数据驱动方法
• Intelligent data-driven solutions for joint communication and sensing (JCS)
  用于联合通信与感知（JCS）的智能数据驱动解决方案
• Large Language Models (LLM) for wireless communications and sensing
  用于无线通信与感知的大语言模型
• Large Language Models (LLM) for network management and optimization
  用于网络管理与优化的大语言模型
• Intelligent telecom-domain open datasets
  智能电信领域开放数据集
• Intelligent network computing for industrial applications
  面向工业应用的智能网络计算

Invited Speakers 拟邀请报告人:

• • Bomin Mao (Professor), Northwestern Polytechnical University,
  毛伯敏，西北工业大学 教授
• • Bingpeng Zhou (Associate Professor), Sun Yat-sen University,
  周炳朋，中山大学，副教授