Zeroing for HW-efficient compressed sensing architectures targeting data compression in wireless sensor networks |
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| Affiliation: | 1. DEI, University of Bologna, Italy;2. ARCES, University of Bologna, Italy;3. ENDIF, University of Ferrara, Italy;4. ISL, ETH Zürich, Switzerland;1. School of Electronic Engineering, Sudan University of Science and Technology, Khartoum, Sudan;2. School of Electrical and Electronic Engineering, Universiti Sains Malaysia, Nibong Tebal 14300, Penang, Malaysia;1. Computer Science Department, Kennesaw State University, Marietta, GA 30060, USA;2. Laboratoire Hubert Curien, University of Lyon, Saint-Etienne, 42000, France;3. Computer Science Department, Bowie State University, Bowie, MD 20715, USA;1. Univ. Grenoble Alpes, LCIS, F-26000, Valence, France;2. Univ. Grenoble Alpes, TIMA, F-38000 Grenoble, France, CNRS, TIMA, F-38000 Grenoble, France;1. Laboratoire Hubert Curien, University of Lyon, 42000 Saint-Etienne, France;2. LCIS, Grenoble Institute of Technology, 26000 Valence, France\n |
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| Abstract: | 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. |
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