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Article

Edge-Based Missing Data Imputation in Large-Scale Environments

1
Institut de Recherche en Informatique de Toulouse, Université de Toulouse III—Paul Sabatier, 31062 Toulouse, France
2
Dipartimento di Matematica e Informatica, Università degli Studi di Palermo, 90123 Palermo, Italy
*
Author to whom correspondence should be addressed.
Academic Editors: Jean-Yves Tigli and Nicolas Ferry
Information 2021, 12(5), 195; https://doi.org/10.3390/info12050195
Received: 11 April 2021 / Revised: 27 April 2021 / Accepted: 28 April 2021 / Published: 29 April 2021
(This article belongs to the Special Issue Smart IoT Systems)
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. View Full-Text
Keywords: edge intelligence; missing data imputation; smart city; urban sensing; multi-agent system; voronoi tessellation; mobile sensing edge intelligence; missing data imputation; smart city; urban sensing; multi-agent system; voronoi tessellation; mobile sensing
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MDPI and ACS Style

Guastella, D.A.; Marcillaud, G.; Valenti, C. Edge-Based Missing Data Imputation in Large-Scale Environments. Information 2021, 12, 195. https://doi.org/10.3390/info12050195

AMA Style

Guastella DA, Marcillaud G, Valenti C. Edge-Based Missing Data Imputation in Large-Scale Environments. Information. 2021; 12(5):195. https://doi.org/10.3390/info12050195

Chicago/Turabian Style

Guastella, Davide Andrea, Guilhem Marcillaud, and Cesare Valenti. 2021. "Edge-Based Missing Data Imputation in Large-Scale Environments" Information 12, no. 5: 195. https://doi.org/10.3390/info12050195

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