Robust Node Localization Based on Distributed Weighted-multidimensional Scaling in Wireless Sensor Networks
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摘要: 研究了多种网络拓扑结构及稀疏网络下节点定位的鲁棒性问题. 联合考虑 1 跳邻居数目、邻居节点自身定位精度与测距误差, 引入节点相对定位误差和相对可信度概念, 提出了一种分布式基于加权多尺度分析技术的鲁棒节点定位算法. 该算法根据节点2跳局部网络连通度信息及邻居节点相对定位误差大小, 自适应选择综合性能好的邻居节点参与迭代优化, 并采用与节点相对可信度成正比的加权机制, 增加高可信度节点在定位计算中的贡献度. 实验数据显示, 该定位算法能够有效地抑制较大定位误差在网络内的扩散, 同基于高斯核加权的 dwMDS(G) 算法相比, 不仅迭代次数减半, 而且在网络连通度较低或拓扑不规则时, 可提高 5% 左右的定位精度.
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关键词:
- 无线传感器网络 /
- 定位 /
- 分布式加权多尺度分析 /
- 自适应邻居选择 /
- 相对可信度
Abstract: This paper focuses on the robustness of node localization in various topological and sparse network. By taking account of the number of 1-hop neighboring nodes, the node position accuracy and the ranging errors, we introduce concepts of node relative localization error and relative reliability, and then propose a robust node localization algorithm based on distributed weighted-multidimensional scaling. It adaptively chooses those neighboring nodes with high relative reliability to join in the node position refinement according to local node density and their relative localization errors within 2 hops, and adopts a weighting scheme proportional to the relative reliability which emphasizes the lowest relative error within the sensor networks. For received signal strength based range measurements, extensive simulation shows that this algorithm can prevent large localization errors from spreading through the networks. Compared with dwMDS(G), this algorithm can decrease iterative times by one half and gain about 5% smaller localization errors in sparse node density or anisotropic topologies. -
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