Qie Youtian, Hao Chuangbo, Song Ping
The Key Laboratory of Biomimetic Robots and Systems, Ministry of Education, Beijing Institute of Technology, Beijing 100081, China.
The Beijing Jinghang Computation and Communication Research Institute, Beijing 100074, China.
Sensors (Basel). 2020 Dec 13;20(24):7146. doi: 10.3390/s20247146.
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.
随着无线传感器网络的广泛应用，具有高采样率的大规模系统越来越普遍。无线传感器网络产生的原始数据量非常大，将所有原始数据传输回主机浪费网络带宽和能量。本文提出了一种基于分层压缩感知和稀疏分解的大数据无线传输方法。该方法包括基于相同稀疏基的分层信号分解方法和带掩码的不同稀疏基分层压缩感知方法。与传统压缩感知方法相比，该方法减少了信号重构误差，降低了信号重构过程中的计算量，减少了硬件资源占用。我们设计了传统压缩感知算法与本文提出的方法之间的对比实验。此外，实验结果证明，与传统压缩感知算法相比，我们提出的方法减少了执行时间以及重构误差，并且能够在相对较低的压缩比下实现更好的重构。
