Parallel Implementation of Feasible Direction Algorithm for Large-Scale Sensor Network Location Problems

In order to improve the deficiency generated from uneven distribution of anchors in the distributed semidefinite programming (SDP) method, improved distributed method is proposed for solving Euclidean metric localization problems that arise from large-scale wireless sensor networks (WSN). By introducing the change of factorization, nonlinear programming (NLP) model is presented on each subarea, and feasible direction algorithm is introduced for solving NLP problems, which can be executed in parallel. Numerical results on large-scale sensor network problems with more than 10000 nodes demonstrate that, the proposed method performs better than the distributed SDP method.