WANG Kai, LIU Yulin, WAN Qun, JING Xiaojun. Compressed Sensing of IR-UWB Wireless Sensor Network Data Based on Two-Dimensional Measurements[J]. Chinese Journal of Electronics, 2015, 24(3): 627-632. doi: 10.1049/cje.2015.07.032
Citation: WANG Kai, LIU Yulin, WAN Qun, JING Xiaojun. Compressed Sensing of IR-UWB Wireless Sensor Network Data Based on Two-Dimensional Measurements[J]. Chinese Journal of Electronics, 2015, 24(3): 627-632. doi: 10.1049/cje.2015.07.032

Compressed Sensing of IR-UWB Wireless Sensor Network Data Based on Two-Dimensional Measurements

doi: 10.1049/cje.2015.07.032
Funds:  This work is supported by the Program for New Century Excellent Talents in University (No.NCET-11-0873), the Program for Innovative Research Team in University of Chongqing (No.KJTD201343), the Key Project of Chongqing Natural Science Foundation (No.CSTC2011BA2016) and the Program for Fundamental and Advanced Research of Chongqing (No.CSTC2013JCYJA40045).
  • Received Date: 2013-12-11
  • Rev Recd Date: 2014-02-12
  • Publish Date: 2015-07-10
  • A novel Compressed sensing (CS) method based on two-dimensional measurements is proposed that can be effectively utilized in Impulse radio ultra-wideband wireless sensor networks (IR-UWB WSNs) to significantly reduce the energy consumption and sampling rate in sensor data transferring.We start by establishing the CS measurement model by taking both spatial and temporal correlations of Wireless sensor network (WSN) data into account. Since our model incorporates a new type of measurement matrix: the block quasi-Toeplitz structured matrix, we derive the Restricted isometry property (RIP) of the block quasi-Toeplitz structured matrix to ensure the performance of the two-dimensional recovery of WSNs data. We substantiate our mathematical analysis by numerical examples in the context of ideal spares vector and realWSN data, and results demonstrate that the approach achieves significantly saving of energy and sampling rate with small reconstruction error.
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