{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,31]],"date-time":"2025-12-31T12:13:27Z","timestamp":1767183207831,"version":"build-2065373602"},"reference-count":27,"publisher":"MDPI AG","issue":"5","license":[{"start":{"date-parts":[[2021,4,29]],"date-time":"2021-04-29T00:00:00Z","timestamp":1619654400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Information"],"abstract":"<jats:p>Smart cities leverage large amounts of data acquired in the urban environment in the context of decision support tools. These tools enable monitoring the environment to improve the quality of services offered to citizens. The increasing diffusion of personal Internet of things devices capable of sensing the physical environment allows for low-cost solutions to acquire a large amount of information within the urban environment. On the one hand, the use of mobile and intermittent sensors implies new scenarios of large-scale data analysis; on the other hand, it involves different challenges such as intermittent sensors and integrity of acquired data. To this effect, edge computing emerges as a methodology to distribute computation among different IoT devices to analyze data locally. We present here a new methodology for imputing environmental information during the acquisition step, due to missing or otherwise out of order sensors, by distributing the computation among a variety of fixed and mobile devices. Numerous experiments have been carried out on real data to confirm the validity of the proposed method.<\/jats:p>","DOI":"10.3390\/info12050195","type":"journal-article","created":{"date-parts":[[2021,4,29]],"date-time":"2021-04-29T10:30:41Z","timestamp":1619692241000},"page":"195","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":18,"title":["Edge-Based Missing Data Imputation in Large-Scale Environments"],"prefix":"10.3390","volume":"12","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-6865-1833","authenticated-orcid":false,"given":"Davide Andrea","family":"Guastella","sequence":"first","affiliation":[{"name":"Institut de Recherche en Informatique de Toulouse, Universit\u00e9 de Toulouse III\u2014Paul Sabatier, 31062 Toulouse, France"}]},{"given":"Guilhem","family":"Marcillaud","sequence":"additional","affiliation":[{"name":"Institut de Recherche en Informatique de Toulouse, Universit\u00e9 de Toulouse III\u2014Paul Sabatier, 31062 Toulouse, France"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4961-2054","authenticated-orcid":false,"given":"Cesare","family":"Valenti","sequence":"additional","affiliation":[{"name":"Dipartimento di Matematica e Informatica, Universit\u00e0 degli Studi di Palermo, 90123 Palermo, Italy"}]}],"member":"1968","published-online":{"date-parts":[[2021,4,29]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"12338","DOI":"10.1109\/ACCESS.2017.2716105","article-title":"Probabilistic Models Toward Controlling Smart-* Environments","volume":"5","author":"Rocher","year":"2017","journal-title":"IEEE Access"},{"key":"ref_2","unstructured":"Ochoa, C.A. 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