Cooperative data reduction in wireless sensor network

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, we 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. We demonstrate that not only substantial energy savings can be achieved but also that an error bound specified by the application can be guaranteed. Moreover, we have investigate the system performance by using the realistic historical soil temperature data as an experimental context. The experimental results demonstrate that the system error meets the specified error tolerance and produces up to a 50 percent of the energy savings compared to several sensing schemes.