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2017年6月12日下午14:30-16:00:Cluster-based Quality-Aware Adaptive Data Compression for Streaming Data

来源: 计算机学院 | 发表时间: 2017-06-08 | 浏览次数: 123

Cluster-based Quality-Aware Adaptive Data Compression for Streaming Data

时间:  2017年6月12日下午14:30-16:00

地点: 三牌楼校区第二会议室

报告人:Dr. Kewei Sha



Dr. Kewei Sha is an Associate Director of Cyber Security Institute and Assistant Professor of Computer Science at University of Houston - Clear Lake (UHCL). Before he moved to UHCL, he was the Department Chair and Associate Professor in the Department of Software Engineering at Oklahoma City University. He received Ph.D in Computer Science from Wayne State University in 2008. His research interests include Internet of Things, Cyber-Physical Systems, Mobile Computing, and Network Security and Privacy. Dr. Sha has served as the secretary of Technical Committee on the Internet of the IEEE Computer Society (IEEE-CS TCI), a guest Editor at Wireless Personal Communications, International Journal on Security and Networks and EAI Transactions on Wireless Spectrum, a conference technical program committee chair for ICCCN 2015, a workshop general chair for ICCCN 2013, a workshop co-chair of MobiPST and MedSPT, a session chair in ICCCN and CollaborateCom, a member of editorial board in several journals, and a program committee member in numerous conferences. He is also a reviewer for numerous journals including IEEE TPDS, IEEE TC, ACM TAAS, IEEE TDSC, IEEE TITS, Elsevier JPDC and so on. He is a member of ACM and a Senior member of IEEE.


Abstract:
Wireless sensor  networks (WSNs) are widely applied in data collection applications. Energy  efficiency is one of the most important design goals of WSNs. In this paper, we  examine the tradeoffs between the energy efficiency and the data quality.  Firstly, four attributes used to evaluate data quality are formally defined.  Then, we propose a novel data compression algorithm, QAAC,  Quality-Aware Adaptive data Compression, to reduce the amount of data  communication so that to save energy. QAAC utilizes an adaptive clustering  algorithm to build clusters from dataset; then a code for each cluster is  generated and stored in a Huffman encoding tree. The encoding algorithm encodes  the original dataset based on the Haffman encoding tree. An  improvement algorithm is also designed to reduce the information loss when data  is compressed. After the encoded data, the Huffman encoding tree and parameters  used in the improvement algorithm have been received at the sink, a  decompression algorithm is used to retrieve the approximation of the original  dataset. The performance evaluation shows that QAAC is  efficient and achieves much higher compression ratio than compared lossy and  lossless compression algorithms, while it has much less information loss than  compared lossy compression algorithms.