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. Multidimensional scaling (MDS) is a recent localization technique that it uses connectivity information. In this paper, we 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. The experiments show the proposed algorithm reduces communication cost, complexity, and convergence time
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