Background: Devices connected to the internet are increasing day by day, and the era of
Internet of Things (IoT) is anticipated. However, handling big data generated by the IoT …

With the increase of new sensing devices in Internet of things (IoT), data dimensions and
types have risen dramatically. The traditional data collection structure cannot satisfy the …

Today, we are awash in a flood of data coming from different data generating sources.
Wireless sensor networks (WSNs) are one of the big data contributors, where data are being …

Although wireless sensor networks (WSNs) have been widely used, the existence of data
loss and corruption caused by poor network conditions, sensor bandwidth, and node failure …

Data reduction is an effective technique for energy saving in wireless sensor networks. It
consists on reducing sensing and transmitting data while conserving a high quality of …

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 …

In this paper, we are interested in the data gathering for Wireless Sensor Networks (WSNs).
In this context, we assume that only some nodes are active in the network, and that these …

Compressed sensing is a revolutionary sampling framework at a sub-Nyquist rate, which
relies potentially on sensing matrix. In this paper, a large class of chaotic sensing matrices …

In this paper, we seek to address the data gathering in the continually growing Wireless
Sensor Networks (WSNs) with the intention to save the nodes' energy. In order to address …

Sparse sensing schemes based on matrix completion for data collection have been
proposed to reduce the power consumption of data-sensing and transmission in wireless …