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Error-Control Truncated SVD Technique for In-Network Data Compression in Wireless Sensor Networks
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DOI：10.1109/ACCESS.2021.3051978.png)
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In-network data compression plays an important role in the elimination of redundant time-series data in a wireless sensor network (WSN). Inconsistency of data and high computational process in cluster formation remain to be challenging issues of in-network data compression particularly for energy-constraint WSNs. This paper develops a new data clustering technique for in-network data preprocessing and compression called Error-Control Truncated Singular Value Decomposition (ETSVD) to achieve online outlier detection and adaptive data compression. The ETSVD is divided into two modules which are Adaptive Recursive Outlier Detection and Smoothing (ARODS) and Adaptive Data Compression (DC). Firstly, the ARODS pre-processes the collected data for outlier detection and smoothing in order to improve the data quality. Secondly, the DC decomposes the pre-processed data into vector space to compress the spatio-temporal correlated data based on the predefined error threshold at the sending end. After the compression of correlated data, the distinct decomposed data are reconstructed at the receiver end which is performed offline. The simulation results show that the proposed technique is able to compress 91.49% of spatio-temporal environmental temperature data with reconstruction error having a minimum tolerance of +/- 1.0 degrees C. The performance improvement of ETSVD in terms of error and accuracy compared to the performance of conventional SVD are 85.26% and 33.49%, respectively. Moreover, the ETSVD provides efficient error-control data preprocessing and compression solutions within the networks with minimum space and time complexities.
Keyword:
Wireless sensor networks
Feature extraction
Data compression
Principal component analysis
Anomaly detection
Noise measurement
Clustering algorithms
Data reduction
data compression
environmental applications
outlier detection
SVD
WSNs
期刊
IF:
3.6
论文数:
9.8W
被引数:
29.4W
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An Efficient Data Compression Model Based on Spatial Clustering and Principal Component Analysis in Wireless Sensor Networks.png)
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