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 …

压缩感知是建立在矩阵分析, 统计概率论, 拓扑几何, 优化与运筹学, 泛函分析等基础上的一种
全新的信息获取与处理的理论框架. 它基于信号的可压缩性, 通过低维空间, 低分辨率, 欠Nyquist …

Compressive sensing is a signal acquisition framework based on the revelation that a small
collection of linear projections of a sparse signal contains enough information for stable …

This article describes a very different approach to the decentralized compression of
networked data. Considering a particularly salient aspect of this struggle that revolves …

Wireless sensor networks (WSNs) are highly resource constrained in terms of power supply,
memory capacity, communication bandwidth, and processor performance. Compression of …

Wireless sensor network applications often involve the computation of pre-defined functions
of the measurements such as for example the arithmetic mean or maximum value. Standard …

It is known that if the objective of a wireless sensor network is not to reconstruct individual
sensor readings at a fusion center but rather to compute a linear function of them, then the …

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 …

Inspired by the theory of compressed sensing and employing random channel access, we
propose a distributed energy-efficient sensor network scheme denoted by Random Access …

Compressive Sensing (CS) shows high promise for fully distributed compression in wireless
sensor networks (WSNs). In theory, CS allows the approximation of the readings from a …