A reliable data compression scheme in sensor-cloud systems based on edge computing
S Lu, Q ** time estimation of frequency-hop** signals based on HMM-enhanced Bayesian compressive sensing with missing observations
H Wang, B Zhang, H Wang, B Wu… - IEEE Communications …, 2022 - ieeexplore.ieee.org
The hop** time reflects the time-varying characteristics of frequency-hop** (FH) signals,
which are essential parameters for the spectrum estimation of FH signals. In this study, we …
which are essential parameters for the spectrum estimation of FH signals. In this study, we …
An energy-efficient sensing matrix for wireless multimedia sensor networks
A measurement matrix is essential to compressed sensing frameworks. The measurement
matrix can establish the fidelity of a compressed signal, reduce the sampling rate demand …
matrix can establish the fidelity of a compressed signal, reduce the sampling rate demand …
Multi-attribute data recovery in wireless sensor networks with joint sparsity and low-rank constraints based on tensor completion
J He, Y Zhou, G Sun, T Geng - IEEE Access, 2019 - ieeexplore.ieee.org
In wireless sensor networks (WSNs), data recovery is an indispensable operation for data
loss or energy constrained WSNs using sparse sampling. However, the recovery accuracy is …
loss or energy constrained WSNs using sparse sampling. However, the recovery accuracy is …
A novel approach based on matrix factorization for recovering missing time series sensor data
X Song, Y Guo, N Li, S Yang - IEEE Sensors Journal, 2020 - ieeexplore.ieee.org
Missing data are unavoidable in wireless sensor networks, due to weather, equipment
failure or some uncontrollable human factors. This results in data sets incompleteness …
failure or some uncontrollable human factors. This results in data sets incompleteness …