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One of the main challenges in wireless sensor networks is to provide low-cost, low-energy reliable data collection. Reliability against transient errors in sensor data can be provided using the model-based error correction described in (S. Mukhopadhyay et al., Mar. 2004), in which temporal correlation in the data is used to correct errors without any overheads at the sensor nodes. In the above work it is assumed that a perfect model of the data is available. However, as variations in the physical process are context-dependent and time-varying in a real sensor network, it is infeasible to have an accurate model of the data properties a priori, thus leading to reduced correction efficiency. In this paper, we address this issue by presenting a scalable methodology for improving the accuracy of data modeling through on-line estimation and model updates. Additionally, we propose enhancements to the data correction algorithm to incorporate robustness against dynamic model changes and potential modeling errors. We evaluate our system through simulations using real sensor data collected from different sources. Experimental results demonstrate that the proposed enhancements lead to an improvement of up to a factor of 10 over the earlier approach.