Sensing of the application environment is the main purpose of a wireless sensor network.
Most existing energy management strategies and compression techniques assume that the …

Wireless sensor networks (WSNs) have taken a giant leap in scale, expanding their
applicability to a large variety of technological domains and applications, ranging from the …

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 …

Wireless sensor network (WSN) in the Internet of Things consists of a large number of
nodes. The proposal of compressive sensing technology provides a novel way for data …

The computationally prohibitive multi-dimensional searching procedure greatly restricts the
application of the maximum likelihood (ML) direction-of-arrival (DOA) estimation method in …

Compressive sensing (CS) can reduce the number of data transmissions and balance the
traffic load throughout networks. However, the total number of transmissions for data …

Data prediction is proposed in wireless sensor networks (WSNs) to extend the system
lifetime by enabling the sink to determine the data sampled, within some accuracy bounds …

The recent advances of compressive sensing (CS) have witnessed a great potential of
efficient compressive data gathering (CDG) in wireless sensor network systems (WSNSs) …

The problem of data sampling and collection in wireless sensor networks (WSNs) is
becoming critical as larger networks are being deployed. Increasing network size poses …

We address the problem of compressing large and distributed signals monitored by a
Wireless Sensor Network (WSN) and recovering them through the collection of a small …