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In this paper, we study designing different threshold protocols that are used to monitor a set of moving objects in k-means computation at a server. In a given data set P, a k-means query returns k points in space, such that average squared distance between each point in p and its nearest center is minimized. Reevaluating k-means every time there is an object update imposes heavy burden on the server and the clients where it reduces the computation and communication costs. The proposed method assigns each moving object a threshold and uses multiple servers for monitoring locations of distinct set of objects and their updates when it crosses the range boundary.