Compressive sensing (CS) has become a popular signal processing technique and has
extensive applications in numerous fields such as wireless communications, image …

Abstract When Donoho and Candes, Rosenberg and Tao separately introduced the concept
of compressive sampling (CS) to the scientific community, the entire basis of traditional …

Recently, distributed data storage (DDS) for Wireless Sensor Networks (WSNs) has attracted
great attention, especially in catastrophic scenarios. Since power consumption is one of the …

Existing compressed sensing (CS)-based spatiotemporal data compression schemes can
significantly decrease communication consumption for data collection; however, they ignore …

Distributed data storage (DDS) provides a promising approach to the reliable recovery of the
whole sensor readings in a wireless sensor network (WSN) by visiting a small subset of …

Wireless sensor networks (WSNs) could benefit a lot from compressive sensing (CS).
Inherent physical structure of sensors of WSNs (battery‐powered devices) demands …

Sensor/Actuator devices are currently being massively adopted, often as nodes of larger
sensor networks. These sensor networks are typically dedicated to context acquisition (eg …

Abstract Wireless Sensor Network (WSN) delivers an important contribution in evolving
fields for example ubiquitous computing and ambient intelligence. Monitoring environment is …

In wireless sensor networks (WSNs), sensor readings have spatiotemporal correlation, and
wireless links are unreliable. To balance the energy-performance trade-off and reduce the …

In this paper, we employ compressive sensing (CS) to design a distributed compressive data
storage (CStorage) algorithm for wireless sensor networks (WSNs). First, we assume that no …