To overcome the faulty data query problem to improve the accuracy of data query, an efficient fault-tolerant event query algorithm (FTEQ) is proposed, which takes the short-term and long-term spatial and temporal similarities between sensors and environment into considerations. An imprecise and missing data correction algorithm based on Kalman filter is proposed to correct fault sensing data, and a score rank algorithm also is proposed to assign each sensor an appropriate value to reflect the importance of sensors. FTEQ performs self-evaluation and cooperative evaluation schemes with its trustful r neighbor nodes to filter fault data query with the importance of sensor. Simulation results prove that FTEQ performs extremely well in terms of faulty detection rate and data query cost.
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