Intelligent Edge-powered Data Reduction: A Systematic Literature Review
Authors: 
Laércio Pioli, Douglas D. J. de Macedo, Daniel G. Costa, Mario A. R. DantasAuthors Info & Claims
Laércio Pioli, Douglas D. J. de Macedo, Daniel G. Costa, Mario A. R. DantasAuthors Info & ClaimsArticle No.: 234, Pages 1 - 39
Abstract
The development of the Internet of Things (IoT) paradigm and its significant spread as an affordable data source has brought many challenges when pursuing efficient data collection, distribution, and storage. Since such hierarchical logical architecture can be inefficient and costly in many cases, Data Reduction (DR) solutions have arisen to allow data preprocessing before actual transmission. To increase DR performance, researchers are using Artificial Intelligence (AI) techniques and models toward reducing sensed data volume. AI for DR on the edge is investigated in this study in the form of a Systematic Literature Review (SLR) encompassing major issues such as data heterogeneity and AI-based techniques to reduce data, architectures, and contexts of usage. An SLR is conducted to map the state of the art in this area, highlighting the most common challenges and potential research trends in addition to a proposed taxonomy.
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Publication History
Published: 25 April 2024
Online AM: 04 April 2024
Accepted: 31 March 2024
Revised: 14 February 2024
Received: 12 January 2023
Published in CSUR Volume 56, Issue 9
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