Authors: Nathan A. Ruprecht; Xinrong Li
Addresses: Department of Electrical Engineering, University of North Dakota, Grand Forks, ND, 58202, USA ' Department of Electrical Engineering, University of North Texas, Denton, TX, 76203, USA
Abstract: Since mid-20th century, Nyquist-Shannon Sampling Theorem is accepted as we need to sample a signal at twice the max frequency component in order to reconstruct it. Compressive sampling (CS) offers a possible solution of sampling sub-Nyquist and reconstructing using convex programming techniques. There has been significant advancements in CS research and development (more notably since mid-2000s in theory and proofs), but still nothing to the advantage of everyday use. There has been little work on hardware in finding realistic constraints of a working CS system used for digital signal processing (DSP) applications. Parameters used in a system are usually assumed based on stochastic models, but not optimised towards a specific application. This paper aims to address a minimal viable platform to implement compressive sensing if applied to a wireless sensor network (WSN), as well as addressing key parameters of CS algorithms to be determined depending on application requirements and constraints.
Keywords: compressive sensing; compressive sampling; WSN; wireless sensor network.
International Journal of Sensor Networks, 2019 Vol.31 No.4, pp.226 - 237
Received: 28 May 2019
Accepted: 28 May 2019
Published online: 26 Oct 2019 *