{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,19]],"date-time":"2026-02-19T15:35:56Z","timestamp":1771515356408,"version":"3.50.1"},"reference-count":41,"publisher":"MDPI AG","issue":"8","license":[{"start":{"date-parts":[[2018,8,20]],"date-time":"2018-08-20T00:00:00Z","timestamp":1534723200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>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\u2019 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.<\/jats:p>","DOI":"10.3390\/s18082735","type":"journal-article","created":{"date-parts":[[2018,8,20]],"date-time":"2018-08-20T11:23:06Z","timestamp":1534764186000},"page":"2735","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["Green Compressive Sampling Reconstruction in IoT Networks"],"prefix":"10.3390","volume":"18","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-1807-2155","authenticated-orcid":false,"given":"Stefania","family":"Colonnese","sequence":"first","affiliation":[{"name":"DIET Department, University of Rome \u201cLa Sapienza\u201d, 00184 Rome, Italy"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Mauro","family":"Biagi","sequence":"additional","affiliation":[{"name":"DIET Department, University of Rome \u201cLa Sapienza\u201d, 00184 Rome, Italy"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Tiziana","family":"Cattai","sequence":"additional","affiliation":[{"name":"DIET Department, University of Rome \u201cLa Sapienza\u201d, 00184 Rome, Italy"},{"name":"Inria, Aramis Project-Team, F-75013 Paris, France"},{"name":"Institut du Cerveau et de la Moelle \u00c9pini\u00e8re, ICM, Inserm U 1127, CNRS UMR 7225, Sorbonne Universit\u00e9, F-75013 Paris, France"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Roberto","family":"Cusani","sequence":"additional","affiliation":[{"name":"DIET Department, University of Rome \u201cLa Sapienza\u201d, 00184 Rome, Italy"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Fabrizio","family":"De Vico Fallani","sequence":"additional","affiliation":[{"name":"Inria, Aramis Project-Team, F-75013 Paris, France"},{"name":"Institut du Cerveau et de la Moelle \u00c9pini\u00e8re, ICM, Inserm U 1127, CNRS UMR 7225, Sorbonne Universit\u00e9, F-75013 Paris, France"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Gaetano","family":"Scarano","sequence":"additional","affiliation":[{"name":"DIET Department, University of Rome \u201cLa Sapienza\u201d, 00184 Rome, Italy"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2018,8,20]]},"reference":[{"key":"ref_1","first-page":"21","article-title":"Energy-valanced scheduling for target tracking in wireless sensor networks","volume":"11","author":"Hu","year":"2014","journal-title":"ACM Trans. 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