Noise Tolerant Localization for Sensor Networks

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.