Efficient Data Gathering in Wireless Sensor Networks Based on Low Rank Approximation

Piao, Xinglin; Hu, Yongli; Sun, Yanfeng; Yin, Baocai

doi:10.1109/greencom-ithings-cpscom.2013.127

Cited by 5 publications

(7 citation statements)

References 23 publications

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“…To obtain better data recovery results, a temporally constrained LRMA model for data collection was proposed in [ 15 ]. The similar idea was shared by our previous work [ 16 ]. However, the spatial properties of the sensing data are not well explored in any currently existing methods.…”

Section: Related Workmentioning

confidence: 84%

“…It is highly desired to investigate the generic data collection method with high energy efficiency. The typical representatives are the recently proposed compressive sensing (CS) based methods [ 18 – 22 ] and the LRMA based methods [ 14 – 16 ].…”

Section: Related Workmentioning

confidence: 99%

“…Although these methods can save certain data communication cost, they would bring extra computational overheads for sensor nodes and so demand considerable sensing cost. Recently the low rank matrix approximation (LRMA) method [ 12 , 13 ] provides a new scheme for data collection in WSNs [ 14 – 16 ]. The main idea of the LRMA based method is that the sensing data of WSNs are regarded as a low rank matrix and recovered from a subset sensing data.…”

Section: Introductionmentioning

confidence: 99%

“…However, the current LRMA based data collection methods [ 14 – 16 ] also suffer from certain inefficiency both at the data sensing stage and the data recovering stage. At the data sensing stage, a random sampling scheme for the selection of sensing nodes is adopted in the current methods [ 14 – 16 ]. According to the LRMA theory [ 13 ], the uniformly random sampling method is an optimized sampling way to get good matrix complement result.…”

Section: Introductionmentioning

confidence: 99%

“…Section 5 elaborates the new optimized node sampling method. We will show the experimental results of our proposed methods compared with state-of-the-art methods, such as basic LRMA model [ 14 ], T-LRMA model [ 15 , 16 ] and S-LRMA model in Section 6. Section 7 concludes the paper with a discussion on future research.…”

Section: Introductionmentioning

confidence: 99%

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Correlated Spatio-Temporal Data Collection in Wireless Sensor Networks Based on Low Rank Matrix Approximation and Optimized Node Sampling

Piao

Sun

et al. 2014

Sensors

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The emerging low rank matrix approximation (LRMA) method provides an energy efficient scheme for data collection in wireless sensor networks (WSNs) by randomly sampling a subset of sensor nodes for data sensing. However, the existing LRMA based methods generally underutilize the spatial or temporal correlation of the sensing data, resulting in uneven energy consumption and thus shortening the network lifetime. In this paper, we propose a correlated spatio-temporal data collection method for WSNs based on LRMA. In the proposed method, both the temporal consistence and the spatial correlation of the sensing data are simultaneously integrated under a new LRMA model. Moreover, the network energy consumption issue is considered in the node sampling procedure. We use Gini index to measure both the spatial distribution of the selected nodes and the evenness of the network energy status, then formulate and resolve an optimization problem to achieve optimized node sampling. The proposed method is evaluated on both the simulated and real wireless networks and compared with state-of-the-art methods. The experimental results show the proposed method efficiently reduces the energy consumption of network and prolongs the network lifetime with high data recovery accuracy and good stability.

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Section: Related Workmentioning

confidence: 84%