Missing and Corrupted Data Recovery in Wireless Sensor Networks Based on Weighted Robust Principal Component Analysis

He, Jingfei; Li, Yunpei; Zhang, Xiaoyue; Li, Jianwei

doi:10.3390/s22051992

Cited by 10 publications

(7 citation statements)

References 33 publications

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“…Recent studies have suggested different shrinkages of singular values based on their importance, which is a potential approach for improving data imputation processes [13]. However, a limited number of studies in the literature have exploited this approach for IoTs.…”

Section: Primary Challenges In 5g-enabled Iotsmentioning

confidence: 99%

“…In practice, the challenges associated with data reconstruction are often complex and involve various forms of corruption, including multiple types of noise, outliers, and missing values. However, previous studies mainly focused on individual types of corruption [11,13]. The corruption of IoT data can occur owing to various factors, including noise, outliers, and missing values.…”

Section: Introductionmentioning

confidence: 99%

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Weighted Robust Tensor Principal Component Analysis for the Recovery of Complex Corrupted Data in a 5G-Enabled Internet of Things

Vo,

Nguyen,

Yoo

2024

Applied Sciences

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Technological developments coupled with socioeconomic changes are driving a rapid transformation of the fifth-generation (5G) cellular network landscape. This evolution has led to versatile applications with fast data-transfer capabilities. The integration of 5G with wireless sensor networks (WSNs) has rendered the Internet of Things (IoTs) crucial for measurement and sensing. Although 5G-enabled IoTs are vital, they face challenges in data integrity, such as mixed noise, outliers, and missing values, owing to various transmission issues. Traditional methods such as the tensor robust principal component analysis (TRPCA) have limitations in preserving essential data. This study introduces an enhanced approach, the weighted robust tensor principal component analysis (WRTPCA), combined with weighted tensor completion (WTC). The new method enhances data recovery using tensor singular value decomposition (t-SVD) to separate regular and abnormal data, preserve significant components, and robustly address complex data corruption issues, such as mixed noise, outliers, and missing data, with the globally optimal solution determined through the alternating direction method of multipliers (ADMM). Our study is the first to address complex corruption in multivariate data using the WTRPCA. The proposed approach outperforms current techniques. In all corrupted scenarios, the normalized mean absolute error (NMAE) of the proposed method is typically less than 0.2, demonstrating strong performance even in the most challenging conditions in which other models struggle. This highlights the effectiveness of the proposed approach in real-world 5G-enabled IoTs.

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Section: Primary Challenges In 5g-enabled Iotsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Weighted Robust Tensor Principal Component Analysis for the Recovery of Complex Corrupted Data in a 5G-Enabled Internet of Things

Vo,

Nguyen,

Yoo

2024

Applied Sciences

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“…For two-dimensional data such as matrix, the low-rank matrix approximate technique is a common choice due to its strong global constraint and strong ability to induce two-dimensional sparse. The Robust Principal Component Analysis (RPCA), whose essence is to use a low-rank constraint to approximate clean data and a sparse regular constraint to eliminate possible noise, has been widely used in background modeling, video, and image signals recovery, et al [4]. However, the RPCA can only process the two-dimensional matrix data, many real-world data like RGB color images and videos, naturally exist in multidimensional array forms (also referred to as tensors).…”

Section: Introductionmentioning

confidence: 99%

Convex Robust Recovery of Corrupted Tensors via Tensor Singular Value Decomposition and Local Low-Rank Approximation

Jiang,

Zhang,

Yuan

et al. 2023

J. Phys.: Conf. Ser.

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This paper discusses the recovery of tensor data corrupted by random noise. Our approach assumes that the potential structure of data is a linear combination of several low-rank tensor subspaces. The goal is to recover exactly these local low-rank tensors and remove random noise as much as possible. Non-parametric kernel smoothing technique is employed to establish an effective mathematical notion of local models. After that, each local model can be robustly separated into a low-rank tensor and a sparse tensor. The low-rank tensor can be recovered by minimizing a weighted combination of the norm and the tensor nuclear norm (TNN) obtained as the tightest convex relaxation of tensor multi-linear rank defined in Tensor Singular Value Decomposition (TSVD). Numerical simulation experiments verify that our proposed approach can effectively denoise the tensor data such as color images with random noise and has superior performance compared to the existing methods.

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“…These nodes are responsible for capturing various forms of multimedia information and facilitating its transmission. However, due to the limited transmission range of each sensor node and the unpredictable wireless channel conditions, data loss and packet errors are common in WMSNs [2].…”

Section: Introductionmentioning

confidence: 99%

Enhancing Packet Reliability in Wireless Multimedia Sensor Networks using a Proposed Distributed Dynamic Cooperative Protocol (DDCP) Routing Algorithm

Al-Jabry,

Al-Asadi

2023

IJEEE

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Wireless Multimedia Sensor Networks (WMSNs) are being extensively utilized in critical applications such as environmental monitoring, surveillance, and healthcare, where the reliable transmission of packets is indispensable for seamless network operation. To address this requirement, this work presents a pioneering Distributed Dynamic Cooperation Protocol (DDCP) routing algorithm. The DDCP algorithm aims to enhance packet reliability in WMSNs by prioritizing reliable packet delivery, improving packet delivery rates, minimizing end-to-end delay, and optimizing energy consumption. To evaluate its performance, the proposed algorithm is compared against traditional routing protocols like Ad hoc On-Demand Distance Vector (AODV) and Dynamic Source Routing (DSR), as well as proactive routing protocols such as Optimized Link State Routing (OLSR). By dynamically adjusting the transmission range and selecting optimal paths through cooperative interactions with neighboring nodes, the DDCP algorithm offers effective solutions. Extensive simulations and experiments conducted on a wireless multimedia sensor node testbed demonstrate the superior performance of the DDCP routing algorithm compared to AODV, DSR, and OLSR, in terms of packet delivery rate, end-to-end delay, and energy efficiency. The comprehensive evaluation of the DDCP algorithm against multiple routing protocols provides valuable insights into its effectiveness and efficiency in improving packet reliability within WMSNs. Furthermore, the scalability and applicability of the proposed DDCP algorithm for large-scale wireless multimedia sensor networks are confirmed. In summary, the DDCP algorithm exhibits significant potential to enhance the performance of WMSNs, making it a suitable choice for a wide range of applications that demand robust and reliable data transmission.

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Missing and Corrupted Data Recovery in Wireless Sensor Networks Based on Weighted Robust Principal Component Analysis

Cited by 10 publications

References 33 publications

Weighted Robust Tensor Principal Component Analysis for the Recovery of Complex Corrupted Data in a 5G-Enabled Internet of Things

Weighted Robust Tensor Principal Component Analysis for the Recovery of Complex Corrupted Data in a 5G-Enabled Internet of Things

Convex Robust Recovery of Corrupted Tensors via Tensor Singular Value Decomposition and Local Low-Rank Approximation

Enhancing Packet Reliability in Wireless Multimedia Sensor Networks using a Proposed Distributed Dynamic Cooperative Protocol (DDCP) Routing Algorithm

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