Data gathering in sensor networks is required to be efficient, adaptable and robust.
Recently, compressive sensing (CS) based data gathering shows promise in meeting these …

The data transmission process in Wireless Sensor Networks (WSNs) often experiences
errors and packet losses due to the environmental interference. In order to address this …

Compressive Sensing (CS) can use fewer samples to recover a great number of original
data, which have a sparse representation in a proper basis. For energy-constrained …

Compressive sensing (CS)-based data aggregation has become an increasingly important
research topic for large-scale wireless sensor networks since conventional data …

In traditional compressed data gathering (CDG) methods of wireless sensor networks
(WSNs), It is assumed that the sparsity of sparse signal is priori and stationary. This …

Compressive sensing (CS) is a novel approach to achieve much lower sampling rate for
sparse signals. In order to reduce the number of data transmissions and save more energy …

Compressive sensing (CS) based data gathering is a promising approach to reduce data
sampling and transmission in wireless sensor networks and thus prolong WSN's lifetime …

Wireless sensor networks (WSNs) is composed of a large number of tiny sensors. These
energy-constrained sensors are deployed in a variety of environments to collect data such …

Compressed sensing (CS) is an emerging sampling technique by which the data sampling
and aggregating can be done simultaneously, which can be applied to many fields …

Compressive sensing (CS)-based in-network data processing is a promising approach to
reduce packet transmission in wireless sensor networks. Existing CS-based data gathering …