Optimization, Artificial Intelligence, and Machine Learning in IoT represent a cutting-edge and dynamic research field that integrates advanced computational techniques with IoT devices. This area focuses on enhancing the intelligence, efficiency and functionality of IoT systems through optimization algorithms, predictive ML models, and AI-driven decision-making models. By exploring these technologies, we can develop smarter, more responsive, and efficient IoT systems that adapt to a wide range of applications, from large-scale smart cities and industrial automation to subminiature implant medical devices. This field offers students a unique opportunity to be at the forefront of technological innovation, solving real-world problems, contributing to the advancement of intelligent systems, and making a significant impact on the future of intelligent environments.

Members

Ningning DING

Assistant Professor

Xudong WANG

Professor

Ying CUI

Associate Professor

Huangxun CHEN

Assistant Professor

Zhilu LAI

Chair Professor

Hong XING

Assistant Professor

Jiadong YU

Assistant Professor

Xinlei HE

Assistant Professor

Danny TSANG

Professor

Kaishun WU

Professor

Liuqing YANG

Chair Professor

Xinhu ZHENG

Assistant Professor

Projects

2020

Competitive Algorithms for Online Budget-Constrained Optimization with Applications to Online Network Resource Allocation

Firm: GRF - RGC - General Research Fund

Time: 2020 -

2019

Harnessing Electric Vehicles for Energy Management Optimization in Sustainable Smart Cities

Firm: RIG - UGC - Research Infrastructure Grant

Time: 2019 -

2019

Harnessing Electric Vehicles for Energy Management Optimization in Sustainable Smart Cities

Firm: GRF - RGC - General Research Fund

Time: 2019 -

Publications

Attention-based QoE-aware Digital Twin Empowered Edge Computing for Immersive Virtual Reality

IEEE Transactions on Wireless Communications, March 2024, article number 10486201, p. 1-1

Yu, Jiadong; Alhilal, Ahmad; Zhou, Tailin; Hui, Pan; Tsang, Hin Kwok

Boosting RF-DC Rectification via Bias Voltage

Proceedings of 2024 IEEE Wireless Power Technology Conference and Expo, WPTCE 2024 / IEEE. Piscataway, NJ : IEEE, 2024, p. 390-394, article number 10557315

Liang, Tingxi; Chiu, Chi Yuk; Gupta, Sanjay; Tsang, Hin Kwok

Combining Conjugate Gradient and Momentum for Unconstrained Stochastic Optimization With Applications to Machine Learning

IEEE Internet of Things Journal, v. 11, (13), July 2024, article number 10472300, p. 23236-23254

Yuan, Yulan; Tsang, Hin Kwok; Lau, Kin Nang

Understanding and Improving Model Averaging in Federated Learning on Heterogeneous Data

IEEE Transactions on Mobile Computing, May 2024, article number 10540229, p. 1-16

Zhou, Tailin; Lin, Zehong; Zhang, Jun; Tsang, Hin Kwok

Multi-resolution Neural Network Compression Based on Variational Bayesian Inference

2024-06, IEEE ICC, Denver, CO