A Novel Distributed Clustering-based MDS Algorithm for Nodes Localization in WSNs

Localization of sensors nodes is a key and fundamental issue in wireless sensor networks due to random deployment. In this paper, we propose a tree based clustering (TBC) multidimensional scaling algorithm for wireless sensor networks with the purpose of overcoming the shortage of classical MDS algorithms in its localization accuracy and computing complexity. Clustering is adopted to degrade the problem scale in our approach and a moderate number of common nodes between clusters are kept during clustering. Inner cluster local coordinates are calculated and then mapped into global coordinates according to the tree structure formed by clustering. Simulations on MATLAB are conducted and the results show that the proposed algorithm has better localization coverage and higher accuracy than the traditional MDS based algorithms.