时间：2019年5月30日（周四）下午5:00—6:00

　　地点：计算所254

　　报告人：肖亮教授, 厦门大学

　　摘要：

　　Mobile edge computing helps healthcare Internet of Things (IoT) devices with energy harvesting provide satisfactory quality of experiences for computation intensive applications. We propose a reinforcement learning (RL) based privacy aware offloading scheme to help healthcare IoT devices protect both the user location privacy and the usage pattern privacy. More specifically, this scheme enables a healthcare IoT device to choose the offloading rate that improves the computation performance, protects user privacy and saves the energy of the IoT device without being aware of the privacy leakage, IoT energy consumption and edge computation model. This scheme uses transfer learning to reduce the random exploration at the initial learning process and applies a Dyna architecture that provides simulated offloading experiences to accelerate the learning process. A post-decision state learning method uses the known channel state model to further improve the offloading performance. We provide the performance bound of this scheme regarding the privacy level, the energy consumption and the computation latency for three typical healthcare IoT offloading scenarios. Simulation results show that this scheme can reduce the computation latency, save the energy consumption, and improve the privacy level of the healthcare IoT device compared with the benchmark scheme.

　　报告人简介：

　　肖亮，厦门大学信息科学与技术学院教授，博士生导师，IEEE高级会员，IEEE ComSoc 大数据技术委员会委员，中国电子学会高级会员，中国计算机学会高级会员，网络与数据通信专委会委员，中国通信学会青年工作委员会委员。从事网络安全，大数据和机器学习等方向的研究。获教育部留学回国人员科研启动基金，入选福建省高等学校新世纪优秀人才支持计划。主持和参与了多项国家自然科学基金和福建省自然科学基金研究项目，参与863项目。出版Springer学术专著和章节3部，获得ICC等多个国际会议最佳论文。担任IEEE Trans. Information Forensics & Security等多个国际SCI期刊编委。担任IEEE Journal on Selected Topics in Signal Processing 等期刊客座编辑。担任INFOCOM、GLOBECOM和ICC等国际学术会议技术议程委员，2019年国际通信旗舰会议ICC的网络与信息安全分会主席。美国Rutgers（新泽西州立）大学电子与计算机工程系博士，清华大学电子系硕士，南京邮电学院通信工程系学士。作为访问学者，曾在普林斯顿大学，弗吉尼亚理工和马里兰大学进行研究。