Distributed sensor network localization using combination and diffusion scheme

A distributed sensor network localization algorithm is presented in this paper. During the localization procedure, each sensor estimates its own coordinate using a local multidimensional scaling (MDS) algorithm. In contrast to the classical MDS algorithm adopting the centralized processing, all the neighbors of each sensor are considered as anchor nodes in the local MDS algorithm. Furthermore, each sensor's coordinate could be estimated by its neighbors based on their respective knowledge. These local estimates are then collected by corresponding sensors and used in a combination step to finally determine sensors' coordinates. In this way, each sensor's knowledge could be diffused and shared in the network. Simulation results show that, compared to the classical MDS algorithm, the proposed algorithm is more robust to measurement noise. Moreover, when sensors are sparsely connected, the distributed MDS algorithm performs better than the centralized version of the MDS algorithm.