Data transmission reduction formalization for cloud offloading-based IoT systems

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Título: Data transmission reduction formalization for cloud offloading-based IoT systems
Autor/es: Elouali, Aya | Mora, Higinio | Mora Gimeno, Francisco José
Grupo/s de investigación o GITE: Arquitecturas Inteligentes Aplicadas (AIA)
Centro, Departamento o Servicio: Universidad de Alicante. Departamento de Tecnología Informática y Computación
Palabras clave: Data transmission reduction | Data transmission reduction | Cloud computing | IoT cameras | Data offloading
Fecha de publicación: 28-mar-2023
Editor: Springer Nature
Cita bibliográfica: Journal of Cloud Computing. 2023, 12:48. https://doi.org/10.1186/s13677-023-00424-8
Resumen: Computation offloading is the solution for IoT devices of limited resources and high-cost processing requirements. However, the network related issues such as latency and bandwidth consumption need to be considered. Data transmission reduction is one of the solutions aiming to solve network related problems by reducing the amount of data transmitted. In this paper, we propose a generalized formal data transmission reduction model independent of the system and the data type. This formalization is based on two main ideas: 1) Not sending data until a significant change occurs, 2) Sending a lighter size entity permitting the cloud to deduct the data captured by the IoT device without actually receiving it. This paper includes the mathematical representation of the model, general evaluation metrics formulas as well as detailed projections on real world use cases.
Patrocinador/es: This work is supported by the Spanish Research Agency (AEI) (https://doi.org/10.13039/501100011033) under project HPC4Industry PID2020-120213RB-I00.
URI: http://hdl.handle.net/10045/133323
ISSN: 2192-113X
DOI: 10.1186/s13677-023-00424-8
Idioma: eng
Tipo: info:eu-repo/semantics/article
Derechos: © The Author(s) 2023. Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.
Revisión científica: si
Versión del editor: https://doi.org/10.1186/s13677-023-00424-8
Aparece en las colecciones:INV - AIA - Artículos de Revistas

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