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This paper presents a robust calibration procedure for clustered wireless sensor networks. Accurate calibration of between-node distances is one crucial step in localizing sensor nodes in an ad-hoc sensor network. The calibration problem is formulated as a parameter estimation problem using a linear calibration model. For reducing or eliminating the unwanted influence of measurement corruptions or outliers on parameter estimation, which may be caused by sensor or communication failures, a robust regression estimator such as the least-trimmed squares (LTS) estimator is a natural choice. Despite the availability of the FAST-LTS routine in several statistical packages (e.g., R, S-PLUS, SAS), applying it to the sensor network calibration is not a simple task. To use the FAST-LTS, one needs to input a trimming parameter, which is a function of the sensor redundancy in a network. Computing the redundancy degree and subsequently solving the LTS estimation both turn out to be computationally demanding. Our research aims at utilizing some cluster structure in a network configuration in order to do robust estimation more efficiently. We present two algorithms that compute the exact value and a lower bound of the redundancy degree, respectively, and an algorithm that computes the LTS estimation. Two examples are presented to illustrate how the proposed methods help alleviate the computational demands associated with robust estimation and thus facilitate robust calibration in a sensor network.