Noise Tolerant Localization for Sensor Networks
Noise Tolerant Localization for Sensor Networks
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DOI：
10.1109/tnet.2018.2852754
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发表时间：
2018-08-01
影响因子：
3.7
通讯作者：
Ahmed, Faraz
中科院分区：
文献类型：
--
作者：
Xiao, Fu;Chen, Lei;Ahmed, Faraz
大多数基于测距的无线传感器网络定位方法依赖于准确和充分的距离测量，但在距离测距中不可避免地会出现噪声和数据丢失。现有的定位方法在距离测量不完整和不完整共存的情况下，定位精度往往不令人满意。在本文中，我们提出了一种容噪定位方案LOMAC来解决这个问题。具体地说，我们首先利用Frobenius范数和L-1范数将含有噪声和缺失的欧氏距离矩阵的重构问题描述为范数正则矩阵补全问题。其次，设计了一种基于乘子交替方向法的求解NRMC问题的高效算法。第三，在已完成的EDM的基础上，进一步采用多维尺度方法对未知节点进行定位。同时，为了加速我们的算法，我们还采用了一些加速技术来降低计算量。大量的实验结果表明，该算法不仅定位性能明显优于已有算法，而且能够提供准确的离群点位置预测，对无线传感器网络的故障诊断具有一定的参考价值。
Most range-based localization approaches for wireless sensor networks (WSNs) rely on accurate and sufficient range measurements, yet noise and data missing are inevitable in distance ranging. Existing localization approaches often suffer from unsatisfied accuracy in the co-existence of incomplete and corrupted range measurements. In this paper, we propose LoMaC, a noise-tolerant localization scheme, to address this problem. Specifically, we first employ Frobenius-norm and L-1-norm to formulate the reconstruction of noisy and missing Euclidean distance matrix (EDM) as a norm-regularized matrix completion (NRMC) problem. Second, we design an efficient algorithm based on alternating direction method of multiplier to solve the NRMC problem. Third, based on the completed EDM, we further employ a multi-dimension scaling method to localize unknown nodes. Meanwhile, to accelerate our algorithm, we also adopt some acceleration techniques to reduce the computation cost. Finally, extensive experimental results show that our algorithm not only achieves significantly better localization performance than prior algorithms but also provides an accurate position prediction of outlier, which is useful for malfunction diagnosis in WSNs.