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A novel heuristic algorithm for node localization in anisotropic wireless sensor networks with holes

机译:一种新型带孔各向异性无线传感器网络中节点定位的启发式算法

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摘要

The node localization is a crucial technology that affects practicality, accuracy and effectiveness of the wireless sensor networks (WSNs). Sensor nodes are often deployed non-uniformly in anisotropic WSNs with holes in various applications such as monitoring area terrain. The existence of holes will invariably affect the Euclidean distances between nodes and result in low accuracy of node localization. In this paper, a Heuristic Multidimensional Scaling (HMDS) algorithm is proposed to improve accuracy of node localization in anisotropic WSNs with holes. By exploring the virtual node and constructing the shortest paths between nodes, the Euclidean distances between nodes are obtained via employing the heuristic approach such that they can be used to calculate more accurate locations of the nodes. The HMDS algorithm greatly reduces the communication complexity and computational complexity compared with the MDS-MAP algorithm. Simulation results demonstrate that the HMDS algorithm requires fewer anchors to obtain the node locations. The HMDS algorithm is suitable for four different topologies, including the semi-C-shape topology, the O-shape topology, the multiple O-shape topology and the concave-shape topology and is exceedingly accurate and efficient comparing with state-of-the-art methods in anisotropic WSNs with holes.
机译:节点定位是一项关键技术,会影响无线传感器网络(WSN)的实用性,准确性和有效性。传感器节点通常在各种应用(例如监视区域地形)中带有孔的各向异性WSN中不均匀地部署。孔的存在将不可避免地影响节点之间的欧几里得距离,并导致节点定位的精度降低。本文提出了一种启发式多维缩放算法(HMDS),以提高带孔各向异性WSN中节点定位的精度。通过探索虚拟节点并构造节点之间的最短路径,可以通过采用启发式方法获得节点之间的欧几里得距离,从而可以将其用于计算节点的更精确位置。与MDS-MAP算法相比,HMDS算法大大降低了通信复杂度和计算复杂度。仿真结果表明,HMDS算法需要较少的锚来获取节点位置。 HMDS算法适用于四种不同的拓扑,包括半C形拓扑,O形拓扑,多个O形拓扑和凹形拓扑,并且与当前状态相比具有极高的准确性和效率。带有孔的各向异性WSN中的先进方法。

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