Zeroing for HW-efficient compressed sensing architectures targeting data compression in wireless sensor networks

Mauro Mangia, Daniele Bortolotti, Fabio Pareschi, Andrea Bartolini, Luca Benini, Riccardo Rovatti, Gianluca Setti

Research output: Contribution to journalArticlepeer-review

17 Scopus citations


The design of ultra-low cost wireless body sensor networks for wearable biomedical monitors has been made possible by today technology scaling. In these systems, a typically multi-channel biosignal sensor takes care of the operations of acquisition, data compression and final output transmission or storage. Furthermore, since these sensors are usually battery powered, the achievement of minimal energy operation is a fundamental issue. To this aim, several aspects must be considered, ranging from signal processing to architectural optimization. In this paper we consider the recently proposed rakeness-based compressed sensing (CS) paradigm along with its zeroing companion. With respect to a standard CS base sensor, the first approach allows us to further increase compression rate without sensible signal quality degradation by exploiting localization of input signal energy. The latter paradigm is here formalized and applied to further reduce the energy consumption of the sensing node. The application of both rakeness and zeroing allows for trading off energy from the compression stage to the transmission or storage one. Different cases are taken into account, by considering a realistic model of an ultra-low-power multicore DSP system.
Original languageEnglish (US)
Pages (from-to)69-79
Number of pages11
JournalMicroprocessors and Microsystems
StatePublished - Feb 1 2017
Externally publishedYes

Bibliographical note

Generated from Scopus record by KAUST IRTS on 2023-02-15

ASJC Scopus subject areas

  • Artificial Intelligence
  • Hardware and Architecture
  • Software
  • Computer Networks and Communications


Dive into the research topics of 'Zeroing for HW-efficient compressed sensing architectures targeting data compression in wireless sensor networks'. Together they form a unique fingerprint.

Cite this