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Wireless sensor networks are a fast-growing class of systems. They offer many new design challenges, due to stringent requirements like tight energy budgets, low-cost components, limited processing resources, and small footprint devices. Such strict design goals call for technologies like nanometer-scale semiconductor design and low-power wireless communication to be used. But using them would also make the sensor data more vulnerable to errors, within both the sensor nodes' hardware and the wireless communication links. Assuring the reliability of the data is going to be one of the major design challenges of future sensor networks. Traditional methods for reliability cannot always be used, because they introduce overheads at different levels, from hardware complexity to amount of data transmitted. This paper presents a new method that makes use of the properties of sensor data to enable reliable data collection. The approach consists of creating predictive models based on the temporal correlation in the data and using them for real-time error correction. This method handles multiple sources of errors together without imposing additional complexity or resource overhead at the sensor nodes. We demonstrate the ability to correct transient errors arising in sensor node hardware and wireless communication channels through simulation results on real sensor data.