Similarity-based data transmission reduction solution for edge-cloud collaborative AI
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http://hdl.handle.net/10045/140966
Título: | Similarity-based data transmission reduction solution for edge-cloud collaborative AI |
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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: | Neural Network splitting | Edge | Cloud |
Fecha de publicación: | dic-2022 |
Editor: | ACM |
Cita bibliográfica: | Aya Elouali, Higinio Mora Mora, and Francisco J. Mora Gimeno. 2022. Similarity-based data transmission reduction solution for edge-cloud collaborative AI. In 2022 5th Artificial Intelligence and Cloud Computing Conference (AICCC) (AICCC 2022), December 17–19, 2022, Osaka, Japan. ACM, New York, NY, USA, 7 pages. https://doi.org/10.1145/3582099.3582107 |
Resumen: | Edge-cloud collaborative processing for IoT data is a relatively new approach that tries to solve processing and network issues in IoT systems. It consists of splitting the processing done by a Neural Network model into edge part and cloud part in order to solve network, privacy and load issues. However, it also has it shortcomings such as the big size of the edge part's output that has to be transmitted to the cloud. In this paper, we are proposing a data transmission reduction method for edge-cloud collaborative solutions that is based on data similarities in stationary objects. The performed experiments proved that we were able to reduce 62% of the data sent. |
URI: | http://hdl.handle.net/10045/140966 |
ISBN: | 978-1-4503-9874-9 |
DOI: | 10.1145/3582099.3582107 |
Idioma: | eng |
Tipo: | info:eu-repo/semantics/conferenceObject |
Derechos: | © 2022 Copyright held by the owner/author(s). Publication rights licensed to ACM. |
Revisión científica: | si |
Versión del editor: | https://doi.org/10.1145/3582099.3582107 |
Aparece en las colecciones: | INV - AIA - Comunicaciones a Congresos, Conferencias, etc. |
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Archivo | Descripción | Tamaño | Formato | |
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Elouali_etal_AICCC2022.pdf | 1,72 MB | Adobe PDF | Abrir Vista previa | |
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