报告题目：Decentralized Context Sharing in Vehicular Delay Tolerant Networks with Compressive Sensing
报告日期及时间： 2016-9-27 星期二上午10：30-11：30
报告地点： B404
报告人： 谢鲲教授
报告人单位：湖南大学
报告人简介:
谢鲲教授为湖南大学计算机应用专业博士，香港理工大学博士后，美国纽约大学石溪分校访问学者。现为湖南大学信息科学与工程学院教授、博士生导师，计算机工程系副主任。研究方向为大数据、云计算和移动云、无线网络与移动计算、计算机网络。目前已主持国家自然科学基金二项，主持湖南省自然科学基金重点基金一项，主持教育部博士点基金一项，作为骨干人员参与国家863计划，973项目，国防基础科研项目，国家科技支撑计划项目。在学科重点期刊和国际顶级会议《IEEE/ACM Transactions on Networking》，《IEEE Transactions on Mobile Computing》，《IEEE Transactions on Computers》，《IEEE Transactions on Wireless Communications》，INFOCOM，ICDCS，SECON，IWQoS，MASS等已发表高水平论文70余篇，申请国家发明专利27项(已经授权16项)，获得国家软件著作权登记4项。荣获2009年布鲁塞尔世界创新科技博览会铜奖，2010年长沙市第十一届自然科学优秀学术论文一等奖，2006年湖南省科学技术进步奖二等奖。
摘要：Vehicles equipped with various types of sensors can act as mobile sensors to monitor the road conditions. To speed up the information collection process, the monitoring data can be shared among vehicles upon their encounters to facilitate drivers to find a good route. The vehicular network experiences intermittent connectivity as a result of the mobility, which makes the inter-vehicle contact duration a scarce resource for data transmissions and the support of monitoring applications over vehicular networks a challenge. We propose a novel compressive sensing (CS)-based scheme to enable efficient decentralized context sharing in vehicular delay tolerant networks, called CS-Sharing. To greatly reduce the data transmission overhead and speed up the monitoring processing, CS-sharing exploits two techniques: sending an aggregate message in each vehicle encounter, and quick collection of information taking advantage of data sharing and the sparsity of events in vehicle networks to significantly reduce the number of measurements needed for global information recovery. We propose a novel data structure, and an aggregation method that can take advantage of the random and opportunistic vehicle encounters to form the measurement matrix. We prove that the measurement matrix satisfies the Restricted Isometry Property (RIP) property required by the CS technique. Our results from extensive simulations demonstrate that CS-Sharing allows vehicles in a large network to quickly obtain the full context data with the successful recovery ratio larger than 90%.
邀请人： 吴黎兵 教授
