STCDG: An Efficient Data Gathering Algorithm Based on Matrix Completion for Wireless Sensor Networks

Cheng, Jie; Ye, Qiang; Jiang, Hongbo; Wang, Dan; Wang, Chonggang

doi:10.1109/twc.2012.121412.120148

Cited by 124 publications

(113 citation statements)

References 20 publications

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“…is the estimate of the complete matrix Y at iteration k and realization n. The initial estimates {X (n) k ∀n} are calculated using (4).…”

Section: Simulation Resultsmentioning

confidence: 99%

“…For instance, in [3] an additional restriction is imposed so that the columns of the recovered matrix are a linear combination of the basis elements in a dictionary. In [4], it is observed that the temperature measurements taken by the sensors in a wireless sensor network are temporally stable in the short term, so a regularization term is added to ensure the short term stability of the recovered data. In the problem of predicting an incomplete matrix of ratings in [5], groups of users with similar background are assumed to have similar preferences, thus a penalty term is included to reduce the variability of the predicted ratings within a group.…”

Section: Introductionmentioning

confidence: 99%

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Matrix completion of noisy graph signals via proximal gradient minimization

Giménez-Febrer

Pagès-Zamora

2017

2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

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Abstract-This paper takes on the problem of recovering the missing entries of an incomplete matrix, which is known as matrix completion, when the columns of the matrix are signals that lie on a graph and the available observations are noisy. We solve a version of the problem regularized with the Laplacian quadratic form by means of the proximal gradient method, and derive theoretical bounds on the recovery error. Moreover, in order to speed up the convergence of the proximal gradient, we propose an initialization method that utilizes the structural information contained in the Laplacian matrix of the graph.

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“…is the estimate of the complete matrix Y at iteration k and realization n. The initial estimates {X (n) k ∀n} are calculated using (4).…”

Section: Simulation Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Matrix completion of noisy graph signals via proximal gradient minimization

Giménez-Febrer

Pagès-Zamora

2017

2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

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“…Figure 8 shows the reconstruction RMSE of RCCSDG, CSDG-OOMP, and CSDG-TV under different dumping ratio and Figure 9 gives the results of PSNR under different sampling ratio. Paper [19] points out that sampling rate is inversely linked to the packet loss rate. This means that the low sampling rate of sensor data is equivalent to the case of high packet loss rate.…”

Section: Reconstruction Performance Simulationmentioning

confidence: 99%

“…So the RCCSDG method can improve the compression ratio dramatically just sacrificing little computational resource. Considering most sensor signals to be of excellent temporal stability in a short time [19], we reshuffle the sensor data only one time and keep the order in this stable period. By this operation, the proposed RCCSDG method can further reduce the energy consumption but ensure the reconstruction accuracy.…”

Section: Introductionmentioning

confidence: 99%

Data Gathering in Wireless Sensor Networks Based on Reshuffling Cluster Compressed Sensing

Zhu

Liu

et al. 2015

International Journal of Distributed Sensor Networks

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The existing compressed sensing (CS) based data gathering (CSDG) methods in wireless sensor networks (WSNs) usually assume that the sensed data are sparse or compressible. However, the sparsity of raw sensed data in some case is not straightforward. In this paper, we present reshuffling cluster compressed sensing based data gathering (RCCSDG) method to achieve both energy efficiency and reconstruction accuracy in WSNs. By incorporating CS into the cluster protocol, RCCSDG is able to reduce the energy consumption and support larger networks. Moreover, the sparsity of raw sensed data can be greatly improved by reshuffling pretreatment. A theoretical analysis to energy consumption of cluster head is performed, and the cost of the pretreatment is small enough to be neglected. Based on these natures, the raw sensed data can be recovered from fewer samples. Also, considering the sensed data to be of excellent temporal stability in a short time, we reshuffle them just one time in this stable period to further reduce the energy consumption of WSNs. In addition, the delay of RCCSDG is analyzed based on TDMA 2 scheduling scheme. We carry out simulations on real sensor datasets. The results show that the RCCSDG can effectively compress the data transmission and decrease energy consumption of WSNs while ensuring the reconstruction accuracy.

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“…Nowadays, the data compressing techniques [9,19,36] enjoy a booming era, which successfully reduce the data transmission cost of sensor nodes. The data compressing techniques can be generally divided into two categories: traditional compressing, and distributed source coding.…”

Section: Introductionmentioning

confidence: 99%

Energy-efficient compressed data aggregation in underwater acoustic sensor networks

et al. 2015

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In this paper, we propose an energy-efficient compressed data aggregation framework for three-dimensional underwater acoustic sensor networks (UASNs). The proposed framework consists of two layers, where the goal is to minimize the total energy consumption of transmitting the data sensed by nodes. The lower layer is the compressed sampling layer, where nodes are divided into clusters. Nodes are randomly selected to conduct sampling, and then send the data to the cluster heads through random access channels. The upper layer is the data aggregation layer, where full sampling is adopted. We also develop methods to determine the number of clusters and the probability that a node participates in data sampling. Simulation results show that the proposed framework can effectively reduce the amount of sampling nodes, so as to reduce the total energy consumption of the UASNs.

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STCDG: An Efficient Data Gathering Algorithm Based on Matrix Completion for Wireless Sensor Networks

Cited by 124 publications

References 20 publications

Matrix completion of noisy graph signals via proximal gradient minimization

Matrix completion of noisy graph signals via proximal gradient minimization

Data Gathering in Wireless Sensor Networks Based on Reshuffling Cluster Compressed Sensing

Energy-efficient compressed data aggregation in underwater acoustic sensor networks

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