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dc.contributor.authorWu, Chun-Haoen_US
dc.contributor.authorTseng, Yu-Cheeen_US
dc.date.accessioned2014-12-08T15:26:45Z-
dc.date.available2014-12-08T15:26:45Z-
dc.date.issued2011-10-01en_US
dc.identifier.issn1536-1233en_US
dc.identifier.urihttp://dx.doi.org/10.1109/TMC.2010.264en_US
dc.identifier.urihttp://hdl.handle.net/11536/19015-
dc.description.abstractWe consider a body-area sensor network (BSN) consisting of multiple small, wearable sensor nodes deployed on a human body to track body motions. Concerning that human bodies are relatively small and wireless packets are subject to more serious contention and collision, this paper addresses the data compression problem in a BSN. We observe that, when body parts move, although sensor nodes in vicinity may compete strongly with each other, the transmitted data usually exist some levels of redundancy and even strong temporal and spatial correlations. Unlike traditional data compression approaches for large-scale and multihop sensor networks, our scheme is specifically designed for BSNs, where nodes are likely fully connected and overhearing among sensor nodes is possible. In our scheme, an offline phase is conducted in advance to learn the temporal and spatial correlations of sensing data. Then, a partial ordering of sensor nodes is determined to represent their transmission priorities so as to facilitate data compression during the online phase. We present algorithms to determine such partial ordering and discuss the design of the underlying MAC protocol to support our compression model. An experimental case study in Pilates exercises for patient rehabilitation is reported. The results show that our schemes reduce more than 70 percent of overall transmitted data compared with previous approaches.en_US
dc.language.isoen_USen_US
dc.subjectBody-area sensor networken_US
dc.subjectdata compressionen_US
dc.subjectinertial sensoren_US
dc.subjectpervasive computingen_US
dc.subjectwireless sensor networken_US
dc.titleData Compression by Temporal and Spatial Correlations in a Body-Area Sensor Network: A Case Study in Pilates Motion Recognitionen_US
dc.typeArticleen_US
dc.identifier.doi10.1109/TMC.2010.264en_US
dc.identifier.journalIEEE TRANSACTIONS ON MOBILE COMPUTINGen_US
dc.citation.volume10en_US
dc.citation.issue10en_US
dc.citation.spage1459en_US
dc.citation.epage1472en_US
dc.contributor.department資訊工程學系zh_TW
dc.contributor.departmentDepartment of Computer Scienceen_US
dc.identifier.wosnumberWOS:000293968000008-
dc.citation.woscount10-
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