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There are various applications in wireless sensor networks which require knowing the relative or actual position of the sensor nodes, but adding GPS receivers or other sophisticated sensors to every node can be expensive. Multi-Dimensional Scaling (MDS) is a recent localization technique that it uses connectivity information. In this paper, the authors propose a new iterative distributed localization algorithm based on Multidimensional Scaling in which each sensor updates its position estimate by minimizing the corresponding local cost function, after taking measurements and initial position estimate from its neighboring nodes.
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