Green Compressive Sampling Reconstruction in IoT Networks

Stefania Colonnese 1 Mauro Biagi 1 Tiziana Cattai 1 Roberto Cusani 1 Fabrizio de Vico Fallani 2 Gaetano Scarano 1
2 ARAMIS - Algorithms, models and methods for images and signals of the human brain
SU - Sorbonne Université, Inria de Paris, ICM - Institut du Cerveau et de la Moëlle Epinière = Brain and Spine Institute
Abstract : In this paper, we address the problem of green Compressed Sensing (CS) reconstruction within Internet of Things (IoT) networks, both in terms of computing architecture and reconstruction algorithms. The approach is novel since, unlike most of the literature dealing with energy efficient gathering of the CS measurements, we focus on the energy efficiency of the signal reconstruction stage given the CS measurements. As a first novel contribution, we present an analysis of the energy consumption within the IoT network under two computing architectures. In the first one, reconstruction takes place within the IoT network and the reconstructed data are encoded and transmitted out of the IoT network; in the second one, all the CS measurements are forwarded to off-network devices for reconstruction and storage, i.e., reconstruction is off-loaded. Our analysis shows that the two architectures significantly differ in terms of consumed energy, and it outlines a theoretically motivated criterion to select a green CS reconstruction computing architecture. Specifically, we present a suitable decision function to determine which architecture outperforms the other in terms of energy efficiency. The presented decision function depends on a few IoT network features, such as the network size, the sink connectivity, and other systems' parameters. As a second novel contribution, we show how to overcome classical performance comparison of different CS reconstruction algorithms usually carried out w.r.t. the achieved accuracy. Specifically, we consider the consumed energy and analyze the energy vs. accuracy trade-off. The herein presented approach, jointly considering signal processing and IoT network issues, is a relevant contribution for designing green compressive sampling architectures in IoT networks.
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Journal articles
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https://hal.inria.fr/hal-01965543
Contributor : Fabrizio de Vico Fallani <>
Submitted on : Wednesday, December 26, 2018 - 12:01:18 PM
Last modification on : Tuesday, April 30, 2019 - 3:44:08 PM

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Stefania Colonnese, Mauro Biagi, Tiziana Cattai, Roberto Cusani, Fabrizio de Vico Fallani, et al.. Green Compressive Sampling Reconstruction in IoT Networks. Sensors, MDPI, 2018, 18 (8), pp.2735. ⟨10.3390/s18082735⟩. ⟨hal-01965543⟩

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