01-实验室简介 About US

工业泛在智能实验室(Industrial Ubiquitous Intelligence Lab)创建于2020年，隶属于南京理工大学电子工程与光电技术学院。其面向通信、计算和控制融合的分布式人工智能，围绕分布式随机优化、多智能体强化学习、分布式机器学习的相关基础理论，及其在未来工业互联网中的应用开展研究。

实验室强调研究问题的原创性和研究成果的影响力，推崇理论与实践相结合的研究方式，并且注重研究成果的标准化和产品化。实验室与国内外知名企业和研究机构有着良好的合作关系，项目资助来源广泛，包括国家自然科学基金、教育部、工信部及其他合作企业。实验室现有教师6名，包括教授3名、副教授1名、讲师2名，在读博士生10名及在读硕士生10名。

02-研究方向 Research

软件定义网络与网络功能虚拟化

移动边缘/雾计算及存储架构的设计与优化

基于区块链的人机物融合、群智感知和激励机制

移动大数据隐私保护技术

无线联邦学习和GAN架构以及其隐私和安全

基于区块链技术的分布式机器学习

03-科研项目 Projects

● 工信部2020年工业互联网创新发展工程，面向典型行业的工业互联网“5G+边缘计算”服务平台项目，2020-2023，17500万（人民币），在研，牵头 ● 工国家自然基金，下一代异构蜂窝网络中面向移动边缘计算架构的计算迁移技术研究、2018-2021，75万(人民币)，在研，主持 ● 工国家重点研发计划“云计算与大数据”重点专项，人机物融合的云计算架构与平台，2018-2021，108万(人民币)，在研，任务负责人

04-最新成果 Papers

● Deyou Zhang, Jun Zhao, Ang Li, Jun Li, IEEE, Branka Vucetic, and Yonghui Li, “Mobile User Trajectory Tracking for IRS Enabled Wireless Networks,” Accepted by IEEE Transactions on Vehicular Technology, 2021. ● Xiongwei Wu, Xiuhua Li, Jun Li, P. C. Ching, Victor C. M. Leung, and H. Vincent Poor, “Caching Transient Content for IoT Sensing: Multi-Agent Soft Actor-Critic,” Accepted by IEEE Transactions on Communications, 2021. ● Dinh C. Nguyen, Ming Ding, Pubudu N. Pathirana, Aruna Seneviratne, Jun Li, Dusit Niyato, and H. Vincent Poor, “Federated Learning for Industrial Internet of Things in Future Industries,” Accepted by IEEE Wireless Communications Magazine, 2021. ● Chuan Ma, Jun Li, Ming Ding, Kang Wei, Wen Chen, and H. Vincent Poor, “Federated Learning with Unreliable Clients: Performance Analysis and Mechanism Design,” Accepted by IEEE Internet of Things Journal, 2021. ● Dinh C. Nguyen, Ming Ding, Pubudu N. Pathirana, Aruna Seneviratne, Jun Li, and H. Vincent Poor, “Federated Learning for Internet of Things: A Comprehensive Survey,” Accepted by IEEE Communications Surveys & Tutorials, 2021. ● Xiaobo Zhou, Shihao Yan, Feng Shu, Riqing Chen, and Jun Li, “UAV-Enabled Covert Wireless Data Collection,” Accepted by IEEE Journal on Selected Areas in Communications, 2021. ● Dinh C. Nguyen, Ming Ding, Quoc-Viet Pham, Pubudu N. Pathirana, Long Bao Le, Aruna Seneviratne, Jun Li, Dusit Niyato, and H. Vincent Poor, “Federated Learning Meets Blockchain in Edge Computing: Opportunities and Challenges,” Accepted by IEEE Internet of Things, 2021. ● Xiongwei Wu, Jun Li, Ming Xiao, Pak-Chung Ching, and H. Vincent Poor, “Multi-Agent Reinforcement Learning for Cooperative Coded Caching via Homotopy Optimization,” Accepted by IEEE Transactions on Wireless Communications, 2021. ● Kang Wei, Jun Li, Ming Ding, Chuan Ma, Hang Su, Bo Zhang, and H. Vincent Poor, “User-Level Privacy-Preserving Federated Learning: Analysis and Performance Optimization,” Accepted by IEEE Transactions on Mobile Computing, 2021. ● Jihao Fan, Jun Li, Jianxin Wang, Zhihui Wei, and Min-Hsiu Hsieh, “Asymmetric Quantum Concatenated and Tensor Product Codes with Large Z-Distances,” IEEE Transactions on Communications, vol. 69, no. 6, pp. 3871-3983, 2021.
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