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Open AccessArticle

Green Compressive Sampling Reconstruction in IoT Networks

DIET Department, University of Rome “La Sapienza”, 00184 Rome, Italy
Inria, Aramis Project-Team, F-75013 Paris, France
Institut du Cerveau et de la Moelle Épinière, ICM, Inserm U 1127, CNRS UMR 7225, Sorbonne Université, F-75013 Paris, France
Author to whom correspondence should be addressed.
Sensors 2018, 18(8), 2735;
Received: 8 July 2018 / Revised: 6 August 2018 / Accepted: 17 August 2018 / Published: 20 August 2018
(This article belongs to the Special Issue Green Communications and Networking for IoT)
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. View Full-Text
Keywords: IoT network; energy efficiency; compressed sensing (CS); CS recovery; sensor networks IoT network; energy efficiency; compressed sensing (CS); CS recovery; sensor networks
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MDPI and ACS Style

Colonnese, S.; Biagi, M.; Cattai, T.; Cusani, R.; De Vico Fallani, F.; Scarano, G. Green Compressive Sampling Reconstruction in IoT Networks. Sensors 2018, 18, 2735.

AMA Style

Colonnese S, Biagi M, Cattai T, Cusani R, De Vico Fallani F, Scarano G. Green Compressive Sampling Reconstruction in IoT Networks. Sensors. 2018; 18(8):2735.

Chicago/Turabian Style

Colonnese, Stefania; Biagi, Mauro; Cattai, Tiziana; Cusani, Roberto; De Vico Fallani, Fabrizio; Scarano, Gaetano. 2018. "Green Compressive Sampling Reconstruction in IoT Networks" Sensors 18, no. 8: 2735.

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