Wireless Transmission Method for Large Data Based on Hierarchical Compressed Sensing and Sparse Decomposition

Abstract

With the widespread application of wireless sensor networks, large-scale systems with high sampling rates are becoming more and more common. The amount of original data generated by the wireless sensor network is very large, and transmitting all the original data back to the host wastes network bandwidth and energy. This paper proposes a wireless transmission method for large data based on hierarchical compressed sensing and sparse decomposition. This method includes a hierarchical signal decomposition method based on the same sparse basis and different sparse basis hierarchical compressed sensing method with a mask. Compared with the traditional compressed sensing method, this method reduces the error of signal reconstruction, reduces the amount of calculation during signal reconstruction, and reduces the occupation of hardware resources. We designed comparison experiments between the traditional compressed sensing algorithm and the method proposed in this article. In addition, the experiments’ results prove that our proposed method reduces the execution time, as well as the reconstruction error, compared with the traditional compressed sensing algorithm, and it can achieve better reconstruction at a relatively low compression ratio.

Deep learning for compressive sensing: a ubiquitous systems perspective

“…Thus, CS is used for fMRI image sampling and reconstruction (Chiew et al 2018;, ultrasound images (Kruizinga 2017;Kim et al 2020a), remote sensing images (Zhao et al 2020a;Wang 2017), and other image-related domains. WSN data sub-sampling and recovery represents another significant area for CS (Xiao et al 2019;Liu et al 2017;Qie et al 2020) as does speech compression and reconstruction (Shawky 2017;Al-Azawi and Gaze 2017).…”

Deep Learning Techniques for Compressive Sensing-Based Reconstruction and Inference -- A Ubiquitous Systems Perspective

“…Thus, CS is used for fMRI image sampling and reconstruction [24,78], ultrasound images [66,68,86], remote sensing images [140,163], and other image-related domains. WSN data sub-sampling and recovery represents another significant area for CS [80,115,144] as does speech compression and reconstruction [5,122].…”

A Novel Complex-Valued Gaussian Measurement Matrix for Image Compressed Sensing

“…By combining the process of sampling and compression, compressed sensing can acquire a signal at a lower sampling rate and accurately recover the original signal, thereby substantially reducing the overhead of data acquisition and storage. After long-term development, compressed sensing has found widespread application in numerous domains, including wireless communication [ 4 ], medical imaging [ 5 ], smart city construction [ 6 ], and video codec [ 7 , 8 ]. Compressed sensing theory primarily consists of three components: the sparse representation of the signal, the measurement matrix’s design, and the reconstruction algorithm’s design.…”