IMF：重心线性定位中迭代最大流节点可定位性检测
IMF: Iterative Max-Flow for Node Localizability Detection in Barycentric Linear Localization
摘要：
确定节点是否可以被唯一定位，称为可定位性检测，是网络定位的一个伴随问题。传统的非线性定位（NLL）模式下的本地化已经得到了很好的探讨，而基于重心坐标的线性定位（BLL）模式下的本地化还没有得到很好的触及。本文分析了现有BLL节点定位理论和算法的不足，提出了BLL节点可定位的一个充分条件和一个必要条件。基于这两个条件，一个有效的迭代最大流（IMF）算法的设计，以确定BLL可定位节点。最后，我们的算法进行了验证，理论分析和实验评估。
英文摘要：
Determining whether nodes can be uniquely localized, called localizability detection, is a concomitant problem of network localization. Localizability under traditional Non-Linear Localization (NLL) schema has been well explored, whereas localizability under the emerging Barycentric coordinate-based Linear Localization (BLL) schema has not been well touched. In this paper, we investigate the deficiency of existing localizability theories and algorithms in BLL, and then propose a necessary condition and a sufficient condition for BLL node localizability. Based on these two conditions, an efficient iterative maximum flow (IMF) algorithm is designed to identify BLL localizable nodes. Finally, our algorithms are validated by both theoretical analysis and experimental evaluations.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
