Novel Data Compression Algorithm for Transmission Line Condition Monitoring

For the problem of data accumulation caused by massive sensor data in transmission line condition monitoring system, this paper analyzes the type and amount of data in the transmission line sensor network, compares the compression algorithms of wireless sensor network data at home and abroad, and pr...

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Veröffentlicht in:Energies (Basel) Jg. 14; H. 24; S. 8275
Hauptverfasser: Liu, Gang, Jia, Lei, Hu, Taishan, Deng, Fangming, Chen, Zheng, Sun, Tong, Feng, Yanchong
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Basel MDPI AG 01.12.2021
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ISSN:1996-1073, 1996-1073
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Zusammenfassung:For the problem of data accumulation caused by massive sensor data in transmission line condition monitoring system, this paper analyzes the type and amount of data in the transmission line sensor network, compares the compression algorithms of wireless sensor network data at home and abroad, and proposes an efficient lossless compression algorithm suitable for sensor data in transmission line linear heterogeneous networks. The algorithm combines the wavelet compression algorithm and the neighborhood index sequence algorithm. It displays a fast operation speed and requires a small amount of calculation. It is suitable for battery powered wireless sensor network nodes. By combining wavelet correlation analysis and neighborhood index sequence coding, the compression algorithm proposed in this paper can achieve a high compression rate, has strong robustness to packet loss, has high compression performance, and can help to reduce network load and the packet loss rate. Simulation results show that the proposed method achieves a high compression rate in the compression of the transmission line parameter dataset, is superior to the existing data compression algorithms, and is suitable for the compression and transmission of transmission line condition monitoring data.
Bibliographie:ObjectType-Article-1
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content type line 14
ISSN:1996-1073
1996-1073
DOI:10.3390/en14248275