About
The Wireless Intelligence and Networked Things Laboratory (WINET) was founded by Prof. Yuguang "Michael" Fang in 2000 in University of Florida and was moved to City University of Hong Kong in 2022. It is currently home to graduate students, postdoctoral researchers, and visiting scholars.
We focus on cutting-edge research in Internet of Things, Wireless Communications, Machine Learning, Computer Vision, Privacy and Security, Health Monitoring, and more. Our goal is to leverage vehicles to build a smarter city.
Research Interests
Internet of Things
- IoT Technologies: Developing advanced IoT frameworks and solutions for smart city applications.
- Wireless Communications: Research on communication protocols and technologies for IoT networks.
Federated Learning
- Distributed Learning: Developing privacy-preserving machine learning techniques where models can be trained across devices without sharing data.
Smart City
- Urban Intelligence: Creating technologies that help cities operate more efficiently through connected data and infrastructure.
Wireless Communication
- Advanced Wireless Protocols: Researching and developing next-generation wireless communication technologies.
Vehicle as a Service (VaaS)
- Connected Vehicles: Exploring how vehicles can serve as mobile service platforms in smart cities.
- Collaborative Perception: Developing systems where vehicles can share sensor data to enhance perception capabilities.
Security & Privacy
- Secure IoT Systems: Developing protection mechanisms for IoT and connected vehicle systems.
- Privacy-Preserving Technologies: Creating methods to protect user data in connected environments.
Health Monitoring
- IoT-based Health Systems: Developing technologies for remote health monitoring and assistance.
Semantic + AI Communications
- Intelligent Information Exchange: Research on leveraging AI for more efficient and contextual communications.
Collaborative Distributed ML
- Multi-agent Systems: Creating frameworks for collaborative machine learning across distributed systems and devices.
Our Team
Our lab is led by Chair Professor of Internet of Things, Fellow of ACM & IEEE & AAAS, Prof. Yuguang "Michael" Fang and consists of talented researchers and PhD students working on cutting-edge research in IoT, wireless communications, and smart city technologies.
Our team includes:
- Distinguished faculty members
- PhD students and researchers
- Former members who have gone on to successful careers
Seminars & Talks
Towards Prevalence of On-Device AI with Full Runtime Adaptability
Speaker:
Prof. Wei Gao (Pittsburgh, USA)
This talk explores adaptable on-device AI for real-time inference and training in resource-limited settings, with applications in healthcare and embodied AI.
Toward Scalable Generative AI via Mixture of Experts in Mobile Edge Networks
Speaker:
Prof. Dusit Niyato (NTU, Singapore)
This talk explores scalable generative AI through mixture of experts in mobile edge networks, addressing challenges in resource utilization when deployed on local user devices.
Accelerator-Centric Edge AI Architectures for Low-Power and Personalized Wearables
Speaker:
Prof. David Atienza (EPFL, Switzerland)
A talk on edge AI architectures for wearable systems, focusing on integrating different families of accelerators for energy-efficient computing.
Quantum Communications, Applications and Challenges
Speaker:
Prof. Jianqing Liu (NCSU, USA)
An exploration of quantum communications principles, applications such as quantum key distribution, and the technical challenges in the field.
A Decoupled Radio Access Networks Architecture for 6G
Speaker:
Prof. Haibo Zhou (NJU, China)
This talk introduces a fully-decoupled radio access network architecture for 6G that enhances spectrum utilization and improves user experience.
RingSFL: An Adaptive Split Federated Learning Towards Taming Client Heterogeneity
Speaker:
Prof. Nan Cheng (Xidian, China)
A presentation on a novel distributed learning scheme that integrates federated learning with a model split mechanism to adapt to client heterogeneity.
Stochastic Cumulative DNN Inference for Intelligent IoT Applications
Speaker:
Prof. Weihua Zhuang (Waterloo, Canada)
A discussion on when to offload DNN inference computation from IoT devices to the edge and how to incorporate multiple inference results to improve accuracy.
Data-Driven Anomaly Detection & Prediction for IoT
Speaker:
Prof. Phone Lin (NTU, Taiwan)
This talk illustrates technologies and solutions for anomaly detection/prediction in IoT systems, along with prototypes and applications.
Enhancing Resource Management and Remote 3D Rendering for Future Wireless Networks
Speaker:
Prof. Chih-Wei Huang (NCU, Taiwan)
This talk examines recent advancements in wireless networks for supporting complex applications like MIMO systems and mixed reality experiences.
