博导个人简介

赵楠：女，博士（后），三级教授，博士生导师。湖北省杰出青年基金获得者、湖北省青年拔尖人才、“3551光谷人才计划”产业教授、湖北工业大学“南湖学者”学术带头人，多次入选Elsevier“全球前2%顶尖科学家”榜单。

主要从事物联网通信、边缘智能、无线大数据等方面研究工作。

项目：

1. 湖北省自然科学基金杰出青年项目、学习-知识融合的移动边缘网络多维资源安全协同优化研究、主持.

2. 湖北省重点研发计划项目、新能源汽车动力电池安全监测大数据分析平台中试研究、主持.

3. 国家自然科学基金青年项目、面向非对称网络信息的协作频谱共享合约机制研究、主持.

4. 湖北省自然科学基金面上项目、认知无线网络中基于重复博弈的动态频谱共享关键技术研究、主持.

5. 湖北省自然科学基金青年项目、基于契约理论的认知无线电协作频谱共享技术研究、主持.

奖励：

1. 大规模物联网高效智能协同关键技术及应用,湖北省科技进步奖二等奖, 2022年.

2. 面向汽车电子组件的视觉引导智能装配关键技术与应用,湖北省科技进步奖二等奖, 2018年.

3. 视频采集与综合管理系统关键技术与应用,湖北省科技进步奖二等奖, 2016年.

代表性论著：

1. N. Zhao, Y. Sun, Y. Pei, D. Niyato. Joint sensing and computation incentive mechanism for mobile crowdsensing networks: A multi-agent reinforcement learning approach. IEEE Internet of Things Journal. 2024. 10.1109/JIOT.2024.3523407.[SCI一区]

2. N. Zhao, Y. Pei, D. Niyato. Incentive mechanism for task offloading and resource cooperation in vehicular edge computing networks: A deep reinforcement learning-assisted contract approach. IEEE Internet of Things Journal. 2024, 11(24): 41098-41109.[SCI一区]

3. N. Zhao, H. Zhu, Y. Sun, Y. Pei, D. Niyato. A contract-based incentive mechanism for joint data sensing and communication in mobile crowdsourcing networks. IEEE Transactions on Vehicular Technology. 2024, 11(73), 17929-17934.[SCI二区]

4. N. Zhao, Y. Pei, Y.-C. Liang, D. Niyato. Deep reinforcement learning-based contract incentive mechanism for joint sensing and computation in mobile crowdsourcing networks. IEEE Internet of Things Journal. 2024, 11(7): 12755-12767.[SCI一区]

5. N. Zhao, Y. Pei, Y.-C. Liang, D. Niyato. A deep reinforcement learning-based contract incentive mechanism for mobile crowdsourcing networks. IEEE Transactions on Vehicular Technology. 2024, 73(3): 4511-4516.[SCI二区]

6. J. Chen, J. Zhang,N. Zhao*, Y. Pei, Y.-C. Liang, D. Niyato. Joint device participation, dataset management, and resource allocation in wireless federated learning via deep reinforcement learning. IEEE Transactions on Vehicular Technology. 2024, 73(3): 4505-4510.[SCI二区]

7. N. Zhao, Y. Pei, Y.-C. Liang, D. Niyato. Multi-agent deep reinforcement learning based incentive mechanism for multi-task federated edge learning. IEEE Transactions on Vehicular Technology. 2023, 72(10): 13530-13535.[SCI二区]

8. N. Zhao, Z. Ye, Y. Pei, Y.-C. Liang, D. Niyato. Multi-agent deep reinforcement learning for task offloading in UAV-assisted mobile edge computing. IEEE Transactions on Wireless Communications, 2022, 21(9): 6949-6960.[SCI一区，ESI高被引]

9. N. Zhao, Y. Liang, D. Niyato, Y. Pei, M. Wu, Y. Jiang. Deep reinforcement learning for user association and resource allocation in heterogeneous cellular networks. IEEE Transactions on Wireless Communications, 2019, 18(11), 5141-5152.[SCI一区，ESI高被引]

10. N. Zhao, Y. Liang, Y. Pei. Dynamic contract incentive mechanism for cooperative wireless networks. IEEE Transactions on Vehicular Technology, 2018, 67(11), 10970-10982.[SCI二区，ESI高被引]

    所授课程：主要承担《现代数字信号处理》等研究生课程教学工作。