Algorithm of Data Compression Based on Multiple Principal Component Analysis over the WSN

Chen, Fenxiong; Wen, Fei; Jia, Hongdong

doi:10.1109/wicom.2010.5601180

Cited by 19 publications

(15 citation statements)

References 5 publications

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“…Many dictionary-based compression algorithms have been developed specifically for data acquisition or sensor networks [10][11][12]. Lempel-Ziv-Welch (LZW) [14] compression is the most popular in the field of lossless data compression due to its simplicity.…”

Section: Fpga Implementationmentioning

confidence: 99%

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Adaptive Sampling for ECG Detection Based on Compression Dictionary

Yuan¹,

Kim²,

Cho

2013

JSTS:Journal of Semiconductor Technology and Science

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Abstract-This paper presents an adaptive sampling method for electrocardiogram (ECG) signal detection. First, by employing the strings matching process with compression dictionary, we recognize each segment of ECG with different characteristics. Then, based on the non-uniform sampling strategy, the sampling rate is determined adaptively. As the results of simulation indicated, our approach reconstructed the ECG signal at an optimized sampling rate with the guarantee of ECG integrity. Compared with the existing adaptive sampling technique, our approach acquires an ECG signal at a 30% lower sampling rate. Finally, the experiment exhibits its superiority in terms of energy efficiency and memory capacity performance.

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Section: Fpga Implementationmentioning

confidence: 99%

“…For example, the power consumption of one bit of data transmitted is approximately equal to running 1000 CPU codes on a sensor node [9,10]. A large amount of samples certainly aggravates the energy burden of data transmission.…”

Section: Introductionmentioning

confidence: 99%

Adaptive Sampling for ECG Detection Based on Compression Dictionary

Yuan¹,

Kim²,

Cho

2013

JSTS:Journal of Semiconductor Technology and Science

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“…Data clustering is mostly utilized to reduce correlated data for achieving energy conservation in WSNs [6][7][8][9]. In particular, several data clustering techniques have been explored including principal component analysis based aggregation (PCAg) [10], multiple-PCA [11], candid covariance-free incremental PCA (CCIPCA) [5], data aggregative window function (DAWF) [12], projection basis PCA [13], distributed PCA [14], K-means [15], enhanced K-means [9], K-medoids [16], singular value decomposition (SVD) [17], auto-regressive moving average (ARMA) [18], and least mean square (LMS) [19]. Various applications of these techniques are available in existing literature [20][21][22][23][24][25][26][27][28].…”

Section: Introductionmentioning

confidence: 99%

Error-Aware Data Clustering for In-Network Data Reduction in Wireless Sensor Networks

Alam

Aziz

Latif

et al. 2020

Sensors

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A wireless sensor network (WSN) deploys hundreds or thousands of nodes that may introduce large-scale data over time. Dealing with such an amount of collected data is a real challenge for energy-constraint sensor nodes. Therefore, numerous research works have been carried out to design efficient data clustering techniques in WSNs to eliminate the amount of redundant data before transmitting them to the sink while preserving their fundamental properties. This paper develops a new error-aware data clustering (EDC) technique at the cluster-heads (CHs) for in-network data reduction. The proposed EDC consists of three adaptive modules that allow users to choose the module that suits their requirements and the quality of the data. The histogram-based data clustering (HDC) module groups temporal correlated data into clusters and eliminates correlated data from each cluster. Recursive outlier detection and smoothing (RODS) with HDC module provides error-aware data clustering, which detects random outliers using temporal correlation of data to maintain data reduction errors within a predefined threshold. Verification of RODS (V-RODS) with HDC module detects not only random outliers but also frequent outliers simultaneously based on both the temporal and spatial correlations of the data. The simulation results show that the proposed EDC is computationally cheap, able to reduce a significant amount of redundant data with minimum error, and provides efficient error-aware data clustering solutions for remote monitoring environmental applications.

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“…It provides a powerful tool for data analysis and pattern recognition which is often used in signal and image processing [4]. Moreover, in the context of sensor networks, the PCA has been investigated to extract features from noisy samples [5], compress and denoise time series measurements [6], [7], as well as for detection of intrusion [8] or anomaly [9].…”

Section: Introductionmentioning

confidence: 99%

In-Network Principal Component Analysis with Diffusion Strategies

Ghadban

Honeiné

Mourad-Chehade

et al. 2016

Int J Wireless Inf Networks

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Principal component analysis is a very well-known statistical analysis technique. It determines principal axes that allow relevant data reduction. Thus, it is very useful in networks where a large number of measures is performed. However, such a technique needs high-complexity computation, which does not scales well in large networks. This paper proposes to overcome this issue, by describing several in-network strategies that estimate the principal axes with lower computational complexity. These strategies are either noncooperative or cooperative with information diffusion. The performance of the proposed strategies are illustrated on diverse applications, such as image processing and dimensionality reduction of time series in wireless sensor networks.

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Algorithm of Data Compression Based on Multiple Principal Component Analysis over the WSN

Cited by 19 publications

References 5 publications

Adaptive Sampling for ECG Detection Based on Compression Dictionary

Adaptive Sampling for ECG Detection Based on Compression Dictionary

Error-Aware Data Clustering for In-Network Data Reduction in Wireless Sensor Networks

In-Network Principal Component Analysis with Diffusion Strategies

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