Abstract
Aiming at the problems of different monitoring data characteristics, limited energy consumption of nodes, and low data compression efficiency in wireless sensor networks, a data compression model based on K-SVD dictionary and compressed sensing is proposed. The model used the K-SVD dictionary learning algorithm to train the sparse base, transferred the sparse transformation from the sensing nodes to the base station, and reduced the energy consumption of the sensing nodes. Compared with the existing OEGMP algorithm and the CS compression algorithm based on DCT sparse basis on the same data set, the experimental results show that the model in this paper has a significant improvement in data compression rate and recovery accuracy.
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Duan, L., Yang, X., Li, A. (2021). WSN Data Compression Model Based on K-SVD Dictionary and Compressed Sensing. In: Zeng, J., Qin, P., Jing, W., Song, X., Lu, Z. (eds) Data Science. ICPCSEE 2021. Communications in Computer and Information Science, vol 1451. Springer, Singapore. https://doi.org/10.1007/978-981-16-5940-9_33
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DOI: https://doi.org/10.1007/978-981-16-5940-9_33
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