Please use this identifier to cite or link to this item: http://hdl.handle.net/11455/43784
標題: Exploring Application-Level Semantics for Data Compression
作者: Tsai, Hsiao-Ping
Yang, De-Nian
Chen, Ming-Syan
關鍵字: Data compression
distributed clustering
object tracking
出版社: IEEE COMPUTER SOC.
摘要: Natural phenomena show that many creatures form large social groups and move in regular patterns. However, previous works focus on finding the movement patterns of each single object or all objects. In this paper, we first propose an efficient distributed mining algorithm to jointly identify a group of moving objects and discover their movement patterns in wireless sensor networks. Afterward, we propose a compression algorithm, called 2P2D, which exploits the obtained group movement patterns to reduce the amount of delivered data. The compression algorithm includes a sequence merge and an entropy reduction phases. In the sequence merge phase, we propose a Merge algorithm to merge and compress the location data of a group of moving objects. In the entropy reduction phase, we formulate a Hit Item Replacement (HIR) problem and propose a Replace algorithm that obtains the optimal solution. Moreover, we devise three replacement rules and derive the maximum compression ratio. The experimental results show that the proposed compression algorithm leverages the group movement patterns to reduce the amount of delivered data effectively and efficiently.
URI: http://hdl.handle.net/11455/43784
ISSN: 1041-4347
Appears in Collections:電機工程學系所

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