Wireless Sensor Network Localization via Matrix Completion Based on Bregman Divergence

Liu, Chunsheng; Shan, Hong; Wang, Bin

doi:10.3390/s18092974

Cited by 9 publications

(5 citation statements)

References 34 publications

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“…However, these methods depend on the choice of relevant parameters and are sensitive to noise. To overcome this problem, a regularized matrix completion model is proposed in [23] introducing the multivariate function Bregman divergence to solve the EDM problem. The major drawback of such a centralized method is its computational cost.…”

Section: Related Workmentioning

confidence: 99%

Beyond Stochastic Gradient Descent for Matrix Completion Based Indoor Localization

et al. 2019

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In this paper, we propose a high accuracy fingerprint-based localization scheme for the Internet of Things (IoT). The proposed scheme employs mathematical concepts based on sparse representation and matrix completion theories. Specifically, the proposed indoor localization scheme is formulated as a simple optimization problem which enables efficient and reliable algorithm implementations. Many approaches, like Nesterov accelerated gradient (Nesterov), Adaptative Moment Estimation (Adam), Adadelta, Root Mean Square Propagation (RMSProp) and Adaptative gradient (Adagrad), have been implemented and compared in terms of localization accuracy and complexity. Simulation results demonstrate that Adam outperforms all other algorithms in terms of localization accuracy and computational complexity.

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Section: Related Workmentioning

confidence: 99%

Beyond Stochastic Gradient Descent for Matrix Completion Based Indoor Localization

et al. 2019

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“…However, it was also too simple to preset the complex noise to these two known types, and the actual noises should be more complicated in practical applications. Recently, Liu et al [21] proposed a Linear Bregman Iteration based matrix completion method to localize node position for WSNs. However, this method did not consider the actual scenario under the co-existence of complex noise and anomaly.…”

Section: Related Workmentioning

confidence: 99%

“…However, it is only the first step of this range-based localization method. The second step of this method is to calculate the location information of the unknown nodes based on the complete distance information and the actual coordinates of anchor nodes, and which can be implemented by using the classical MDS method [6,8,21].…”

Section: Anomaly-aware Node Localization For Wsnsmentioning

confidence: 99%

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ANLoC: An Anomaly-Aware Node Localization Algorithm for WSNs in Complex Environments

Cui

Chen

2019

Sensors

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Accurate and sufficient node location information is crucial for Wireless Sensor Networks (WSNs) applications. However, the existing range-based localization methods often suffer from incomplete and detorted range measurements. To address this issue, some methods based on low-rank matrix recovery have been proposed, which usually assume noises follow single Gaussian distribution or/and single Laplacian distribution, and thus cannot handle the case with wider noise distributions beyond Gaussian and Laplacian ones. In this paper, a novel Anomaly-aware Node Localization (ANLoC) method is proposed to simultaneously impute missing range measurements and detect node anomaly in complex environments. Specifically, by utilizing inherent low-rank property of Euclidean Distance Matrix (EDM), we formulate range measurements imputation problem as a Robust ℓ 2 , 1 -norm Regularized Matrix Decomposition (RRMD) model, where complex noise is fitted by Mixture of Gaussian (MoG) distribution, and node anomaly is sifted by ℓ 2 , 1 -norm regularization. Meanwhile, an efficient optimization algorithm is designed to solve proposed RRMD model based on Expectation Maximization (EM) method. Furthermore, with the imputed EDM, all unknown nodes can be easily positioned by using Multi-Dimensional Scaling (MDS) method. Finally, some experiments are designed to evaluate performance of the proposed method, and experimental results demonstrate that our method outperforms three state-of-the-art node localization methods.

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“…The matrix completion problem attempts to recover a low-rank or an approximate low-rank matrix by observing only partial elements [1]. In recent years, many strong theoretical analyses have been developed on the matrix completion problem [2][3][4][5][6][7], which has been applied to a wide variety of practical applications such as background modeling [8,9], recommender systems [10], sensor localization [11,12], image and video processing [13,14], and link prediction [15]. In particular, these all results are based on a potential assumption that the observed entries are continuous-valued.…”

Section: Introductionmentioning

confidence: 99%

Binary Matrix Completion With Nonconvex Regularizers

Liu

Shan

2019

IEEE Access

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Many practical problems involve the recovery of a binary matrix from partial information, which makes the binary matrix completion (BMC) technique received increasing attention in machine learning. In particular, we consider a special case of BMC problem, in which only a subset of positive elements can be observed. In recent years, convex regularization based methods are the mainstream approaches for this task. However, the applications of nonconvex surrogates in standard matrix completion have demonstrated better empirical performance. Accordingly, we propose a novel BMC model with nonconvex regularizers and provide the recovery guarantee for the model. Furthermore, for solving the resultant nonconvex optimization problem, we improve the popular proximal algorithm with acceleration strategies. It can be guaranteed that the convergence rate of the algorithm is in the order of 1/T , where T is the number of iterations. Extensive experiments conducted on both synthetic and real-world data sets demonstrate the superiority of the proposed approach over other competing methods.Binary matrix completion, Link prediction, Nonconvex regularizers, Topology inference 1 δ 2 √ mn , where δ denotes the sampling rate of positive entries.• We develop an accelerated version of the proximal algorithm for solving the resultant nonconvex optimization model. It can be guaranteed that the proposed algorithm has a convergence rate of O (1/T ), where T denotes the number of iterations.• We implement and analyze the proposed algorithm on both synthetic and real-world data sets. Experimental results demonstrate the superiority of the resultant algorithm to state-of-the-art methods.

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Wireless Sensor Network Localization via Matrix Completion Based on Bregman Divergence

Cited by 9 publications

References 34 publications

Beyond Stochastic Gradient Descent for Matrix Completion Based Indoor Localization

Beyond Stochastic Gradient Descent for Matrix Completion Based Indoor Localization

ANLoC: An Anomaly-Aware Node Localization Algorithm for WSNs in Complex Environments

Binary Matrix Completion With Nonconvex Regularizers

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