Range-Based Localization for Sparse 3-D Sensor Networks
Range-Based Localization for Sparse 3-D Sensor Networks
复制标题
稀疏 3D 传感器网络的基于范围的定位
DOI：
10.1109/jiot.2018.2856267
复制
发表时间：
2019-02-01
影响因子：
10.6
通讯作者：
Wang, Kun
中科院分区：
文献类型：
--
作者：
Liu, Xuan;Yin, Jiangjin;Wang, Kun
定位在无线传感器网络中起着至关重要的作用。针对二维传感器网络或密集部署的三维传感器网络，提出了许多基于距离的定位算法。然而，在稀疏三维传感器网络中，基于距离的定位仍然是一个具有挑战性的问题，因为网络的稀疏性使得难以获得适当的节点顺序进行顺序定位。补丁-拼接定位策略可以克服二维网络的稀疏性问题，但对于三维网络，在公共节点不足的情况下，如何唯一地合并两个补丁仍然是一个未知的问题。在本文中，我们通过推导两个子网可以唯一合并的条件来解决这个具有挑战性的问题。在提出的方法中，我们将平移参数视为未知数，并形成一组未知数可以唯一解的方程。该算法的新颖之处还在于，我们利用相邻子网之间的公共节点和连接边来合并它们，从而使两个子网合并的几率非常高。我们进行了大量的仿真实验来评估所提出算法的性能。结果表明，在平均节点度为11、锚定比为5%的稀疏三维网络中，本文算法可以定位90%以上的节点，而在相同情况下，现有最佳方案只能定位52%的节点。
Localization plays a pivotal role in wireless sensor networks. Many range-based localization algorithms have been proposed for 2-D sensor networks or densely deployed 3-D sensor networks. However, range-based localization in sparse 3-D sensor networks is still a challenging problem, because the sparseness of the network makes it difficult to obtain a proper order of nodes to be sequentially localized. The patch-and-stitching localization strategy can conquer the sparseness problem in 2-D networks, but for 3-D networks it is still unknown how to uniquely merge two patches when there are not enough common nodes. In this paper, we solve this challenging problem by deriving the conditions under which two subnetworks can be uniquely merged. In the proposed approach, we treat the translation parameters as unknowns and form a set of equations with which the unknowns can be uniquely solved. The novelty of our algorithm also lies in that we exploit both common nodes and connecting edges among adjacent subnetworks to merge them, resulting in very high chances that two subnetworks can be merged. We conduct extensive simulation experiments to evaluate the performance of the proposed algorithm. The results show that the proposed algorithm could localize more than 90% of nodes in sparse 3-D networks with average node degree of 11 and anchor ratio of 5%, while the best existing solution can localize only 52% of nodes in the same situation.