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Due to the limited power constraint in sensors, dynamic scheduling with data quality management is strongly preferred in long lifetime monitoring applications. But typical techniques treat data management as an isolated process on only selected individual nodes, e.g. the centroid node. In this paper, the authors propose and evaluate an aggressive data reduction algorithm based on error inference within sensor segments. The architecture integrates three parallel dynamic error control mechanisms to optimize the trade-off between energy saving and data validity. They demonstrate that not only substantial energy savings can be achieved but also that an error bound specified by the application can be guaranteed. Moreover, they have investigate the system performance by using the realistic historical soil temperature data as an experimental context.
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