A novel optimization method for WSN based on mixed matrix decomposition of NMF and 2-SVD-QR

Zhao, Zhiyang; Zhang, Baoju; Zhang, Bo; Zhang, Cuiping; Wang, Man; Yan, Wenrui; Wang, Fengjuan

doi:10.1016/j.adhoc.2021.102454

Cited by 7 publications

(4 citation statements)

References 36 publications

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“…The cost of the energy is still affected and ultimately affects the communication in Distributed data in WSN [19]. The motivation behind employing data compression in WSNs is to mitigate issues related to communication overload, energy consumption, and network lifetime [5][6][7]. Data compression techniques, particularly Non-Negative Matrix Factorization (NMF), are used to transform unstructured data into a more interpretable format suitable for communication.…”

Section: Introductionmentioning

confidence: 99%

“…Data compression techniques, particularly Non-Negative Matrix Factorization (NMF), are used to transform unstructured data into a more interpretable format suitable for communication. As seen in [5][6][7]19], is chosen for its ability to handle the spectral representation of data and efficiently identify faulty nodes, reducing false measurements and improving overall network lifetime. Hence, considering this, the study is motivated to use the Matrix factorization-based technique to compress the data at the required position to improve the process of CH selection and reduce energy costs.…”

Section: Introductionmentioning

confidence: 99%

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CCOA-DC: A Novel Optimization with NMF Data Compression in WSN Data Aggregation

2024

IJIES

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Wireless Sensor Networks (WSNs) play a pivotal role in remote monitoring, surveillance, and Internet of Things (IoT) applications. The efficient utilization of battery-powered sensor nodes in WSNs, given their limited power capacity, is crucial for successful data transmission. Conventional clustering algorithms while efficient in clustering, often lacks the efficient management of data generated by sensor nodes, leading to redundant data in applications like IoT leading to reduced network lifetime. To overcome this issue, this paper introduces a novel approach, named CCOA-DC (Improved Coati Optimization with Cognitive Factor (CCOA) through Data Compression (DC)), in clustering heterogeneous aggregated WSN data. The research unfolds in two novel phases. Initially, a non-negative matrix factorization (NMF) model is introduced data compression for clustering, addressing the challenge of data transmission and energy efficiency. Subsequently, the performance is enhanced through load balancing, featuring dynamic cluster head selection via Improved Coati Optimization (COA) with cognitive factor (C), denoted as CCOA. A distinctive aspect is the incorporation of the NMF data compression technique in both clustering and cluster head selection processes, introducing an energy-efficient, load-balanced, and compressed data aggregation mechanism. The proposed CCOA-DC undergoes rigorous testing, comparing its performance against existing models to validate its superiority. Comparative analyses with renowned models such as TCBDGA, HEED, and FEEC-IIR underscore the distinct advantages of CCOA-DC. Notably, it achieves a reduction of 78.57% of packet loss ratio compared to FEEC-IIR model. The model achieves high packet delivery ratio which is 98.67%, and shows optimized energy consumption of 68.01% Joules. This novel compression-based metaheuristic data aggregation algorithm showcases its effectiveness in addressing the energy conservation challenge, affirming its prominence in the area of WSNs based IoT applications.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

CCOA-DC: A Novel Optimization with NMF Data Compression in WSN Data Aggregation

2024

IJIES

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“…In view of the problems of large network data and difficult to choose, the recommendation system based on deep learning [1] came into being, which is widely used in news, movies, music, advertising and other fields. Most of the classic recommendation algorithms model user preferences based on the user's historical interaction information on the item, which is called collaborative filtering [2] . Therefore, some researchers have proposed a collaborative filtering based on matrix decomposition algorithm [3] , which uses the internal product method to obtain the user's interest in items by generating user hidden vectors and item hidden vectors, and strengthens the ability of the model to process sparse matrices.…”

Section: Introductionmentioning

confidence: 99%

Research on recommendation algorithm based on neural network fusing user behavior sequence

Wang

Wang²

2023

Third International Conference on Artificial Intelligence and Computer Engineering (ICAICE 2022)

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The traditional neural network collaborative filtering algorithm is developed from the matrix decomposition algorithm, and compared with the matrix decomposition, the neural network collaborative filtering algorithm uses a multi-layer perceptron to replace the original dot product operation. Although this method can fully cross user vectors and item vectors, it cannot learn the timing problems on the user side. To solve this, a collaborative filtering model based on deep neural network fusion user sequence is proposed. The model still models the interaction information between the user and the object, with the difference being that recurrent neural networks are introduced when modeling the user. The probability of the user's interest in the item is calculated from the obtained user characteristics and item characteristics. Experiments on the MovieLens and MIND datasets show that the proposed model is higher than the matrix decomposition algorithm and neural network co-filtering algorithm on the AUC and F1 Score indicators, which verifies the accuracy of the model's recommendation effect.

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“…Internet of Things (IoT) [25,26] is another typical application area of the SVD compression. As a crucial technique breakthrough intensively applied in areas such as transportation [27], smart building [28] and energy management [29], huge amounts of data poses enormous pressure for efficient computation and fast transmission, to solve which SVD generates a serious of efficient compression methods [30,31,32,33,34]. Those SVD-based compression approaches even became state-of-the-art technologies in the smart grid [35].…”

Section: Introductionmentioning

confidence: 99%

Enhancing the SVD Compression

Wang¹,

Zhang²,

Zhao³

2021

Preprint

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Orthonormality is the foundation of matrix decomposition. For example, Singular Value Decomposition (SVD) implements the compression by factoring a matrix with orthonormal parts and is pervasively utilized in various fields. Orthonormality, however, inherently includes constraints that would induce redundant degrees of freedom, preventing SVD from deeper compression and even making it frustrated as the data fidelity is strictly required. In this paper, we theoretically prove that these redundancies resulted by orthonormality can be completely eliminated in a lossless manner. An enhanced version of SVD, namely E-SVD, is accordingly established to losslessly and quickly release constraints and recover the orthonormal parts in SVD by avoiding multiple matrix multiplications. According to the theory, advantages of E-SVD over SVD become increasingly evident with the rising requirement of data fidelity. In particular, E-SVD will reduce 25% storage units as SVD reaches its limitation and fails to compress data. Empirical evidences from typical scenarios of remote sensing and Internet of things further justify our theory and consistently demonstrate the superiority of E-SVD in compression. The presented theory sheds insightful lights on the constraint solution in orthonormal matrices and E-SVD, guaranteed by which will profoundly enhance the SVD-based compression in the context of explosive growth in both data acquisition and fidelity levels.

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A novel optimization method for WSN based on mixed matrix decomposition of NMF and 2-SVD-QR

Cited by 7 publications

References 36 publications

CCOA-DC: A Novel Optimization with NMF Data Compression in WSN Data Aggregation

CCOA-DC: A Novel Optimization with NMF Data Compression in WSN Data Aggregation

Research on recommendation algorithm based on neural network fusing user behavior sequence

Enhancing the SVD Compression

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