Abstract
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.
Highlights
IntroductionPublisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations
For Sensor-Lempel Ziv Welch (S-LZW), Lossless entropy compression (LEC) has better compression performance, but because LEC is a static data compression algorithm, which is suitable for unified data compression after sensor node sampling and collection, it cannot adapt to dynamic data compression in transmission line online monitoring system
Firstly,structure the transmission line condition monitoring system, and the data transmission network m redundant data are removed by wavelet transform, and the data are compressed by dynamic compression algorithm
Summary
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations. For S-LZW, LEC has better compression performance, but because LEC is a static data compression algorithm, which is suitable for unified data compression after sensor node sampling and collection, it cannot adapt to dynamic data compression in transmission line online monitoring system. The adaptive prediction process in ALFC does not need to define the filter coefficients in advance, and allows the system to dynamically adjust according to the changes of data sources This method can adapt to Energies 2021, 14, 8275 the specific certainty of online monitoring data over time and eliminate more redundant data. Considering the correlation of transmission line monitoring data in the time dimension, the space dimension and the parameter dimension can further eliminate redundancy, improve the compression rate and reduce network transmission energy consumption. Proved through parameter correlation, so as to reduce the amount of data transmissi the network, reduce node energy consumption and prolong network life
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