SpaseLoc: An adaptive subproblem algorithm for scalable wireless sensor network localization

被引：41

作者：

Carter, Michael W. ^{[1
]}

Jin, Holly H.

Saunders, Michael A.

Ye, Yinyu

机构：

[1] Univ Toronto, Dept Mech & Ind Engn, Toronto, ON M5S 3G8, Canada

[2] Stanford Univ, Dept Management Sci & Engn, Stanford, CA 94305 USA

来源：

SIAM JOURNAL ON OPTIMIZATION | 2006年 / 17卷 / 04期

关键词：

sensor localization; semidefinite programming; large-scale optimization;

D O I：

10.1137/040621600

中图分类号：

O29 [应用数学];

学科分类号：

070104 ;

摘要：

An adaptive rule-based algorithm, SpaseLoc, is described to solve localization problems for ad hoc wireless sensor networks. A large problem is solved as a sequence of very small subproblems, each of which is solved by semidefinite programming relaxation of a geometric optimization model. The subproblems are generated according to a set of sensor/anchor selection rules. Computational results compared with existing approaches show that the SpaseLoc algorithm scales well and provides excellent localization accuracy.

引用

页码：1102 / 1128

页数：27