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In many applications of Wireless Sensor Networks (WSNs), it is necessary to continuously extract data from the networks. It is expensive to obtain all sensor readings. Clustering and prediction techniques, which exploit spatial and temporal correlation among the sensor data, provide opportunities for reducing the energy consumption of continuous sensor data collection. Integrating clustering and prediction techniques makes it essential to design a new data collection scheme, so as to achieve network energy efficiency and stability. A cluster head represents all sensor nodes in the cluster and collects data values from them. To realize prediction techniques efficiently in WSNs, an adaptive scheme to control prediction is used.
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