Compressive Sparse Data Gathering With Low-Rank and Total Variation in Wireless Sensor Networks.
Yi XuGuiling SunTianyu GengBowen ZhengPublished in: IEEE Access (2019)
Keyphrases
- data gathering
- low rank
- total variation
- wireless sensor networks
- convex optimization
- minimization problems
- low rank matrix
- nuclear norm
- rank minimization
- compressive sensing
- norm regularization
- image denoising
- image restoration
- denoising
- energy efficient
- matrix completion
- sensor networks
- group lasso
- regularization term
- regularization methods
- random projections
- interior point methods
- sparse representation
- energy consumption
- linear combination
- base station
- image processing
- singular value decomposition
- primal dual
- high order
- kernel matrix
- sensor nodes
- multi hop
- missing data
- high dimensional data
- singular values
- routing algorithm
- matrix factorization
- sparse matrix
- natural images
- structured sparsity
- compressed sensing
- multiscale
- routing protocol
- computer vision