📚︎ Track 1: IoT Networking and Edge Systems

• 5G/6G IoT network architecture

• Massive-access IoT resource scheduling

• Cooperative communication for IoT systems

• Energy-efficient wireless IoT networking

• URLLC-oriented critical IoT transmission

• SDN-enabled flexible IoT infrastructures

• Self-organizing IoT network protocols

• Edge computing framework for IoT

• Heterogeneous IoT interoperability

• IoT network quality of service optimization

📚︎ Track 2: Multimedia IoT and Information Assurance

• Multimedia transmission over mobile IoT networks

• Privacy-preserving multimedia data sharing

• Multimedia content authentication and forensics

• Adversarial detection for multimedia streams

• Trusted multimedia distribution in ad-hoc networks

• Lightweight cryptography for edge multimedia

• IoT denial-of-service attack mitigation

• Multimedia integrity verification

• Secure mobile multimedia sensing

• Information governance for multimedia IoT

📚︎ Track 3: Fundamental Machine Learning Methods

• Neural network model optimization

• Distributed machine learning algorithms

• Pattern recognition and feature learning

• Transfer and few-shot learning

• Adversarial machine learning

• Deep learning model compression

• Graph learning and representation learning

• Ensemble learning theory and methods

• Online and incremental learning

• Unsupervised and self-supervised learning

📚︎ Track 4: Machine Learning Applications for IoT

• Edge ML for IoT sensing data analysis

• ML-driven IoT anomaly diagnosis

• ML-based IoT resource allocation

• Pattern recognition on IoT streaming data

• Federated learning for distributed IoT

• ML-enabled IoT predictive maintenance

• ML for intelligent IoT control

• Multimodal learning for IoT perception

• ML-based IoT security threat detection

• ML for IoT digital twin modelling