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In a resource-constrained Wireless Sensor Networks (WSNs), the optimization of the sampling and the transmission rates of each individual node is a crucial issue. A high volume of redundant data transmitted through the network will result in collisions, data loss, and energy dissipation. This paper proposes a novel data reduction scheme that exploits the spatial-temporal correlation among sensor data in order to determine the optimal sampling strategy for the deployed sensor nodes. Thisdoi:10.1109/access.2019.2910886 fatcat:exwag43tjvebpff7ti5rtgclze