Intelligent Edge-powered Data Reduction: A Systematic Literature Review

被引：4
作者：
Pioli, Laercio [1 ]
de Macedo, Douglas D. J. [2 ]
Costa, Daniel G. [3 ]
Dantas, Mario A. R. [4 ]
机构：
[1] Univ Fed Santa Catarina, Comp Sci, Florianopolis, SC, Brazil
[2] Univ Fed Santa Catarina, Dept Informat Sci, Florianopolis, SC, Brazil
[3] Univ Porto, FEUP, Porto, Portugal
[4] Univ Fed Juiz de Fora, Dept Informat & Stat, Juiz De Fora, Brazil
来源：
ACM COMPUTING SURVEYS | 2024年 / 56卷 / 09期
关键词：
Internet of things; artificial intelligence; data reduction; intelligent edge; systematic literature review; edge computing; DATA ANALYTICS; COMPRESSION; ENERGY; INTERNET; NETWORK; THINGS; MODEL;
D O I：
10.1145/3656338
中图分类号：
TP301 [理论、方法];
学科分类号：
081202 ;
摘要：
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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页数：39
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