Wireless sensor networks (WSNs) monitor dynamic environments that change rapidly over
time. This dynamic behavior is either caused by external factors or initiated by the system …

Complementary to the fancy big data applications, networking for big data is an
indispensable supporting platform for these applications in practice. This emerging research …

In the past five years, deep learning methods have become state-of-the-art in solving various
inverse problems. Before such approaches can find application in safety-critical fields, a …

The emerging compressed sensing (CS) theory can significantly reduce the number of
sampling points that directly corresponds to the volume of data collected, which means that …

Gossip algorithms are attractive for in-network processing in sensor networks because they
do not require any specialized routing, there is no bottleneck or single point of failure, and …

In recent years, compressed sensing (CS) has attracted considerable attention in areas of
applied mathematics, computer science, and electrical engineering by suggesting that it may …

In this paper, we model the components of the compressive sensing (CS) problem, ie, the
signal acquisition process, the unknown signal coefficients and the model parameters for the …

This paper presents the first complete design to apply compressive sampling theory to
sensor data gathering for large-scale wireless sensor networks. The successful scheme …

In this thesis, I propose and study an efficient algorithm for solving a class of compressive
sensing problems with total variation regularization. This research is motivated by the need …

Data collection is a crucial operation in wireless sensor networks. The design of data
collection schemes is challenging due to the limited energy supply and the hot spot problem …