Intelligent Edge-powered Data Reduction: A Systematic Literature Review-Reference-Cited by-同舟云学术

Intelligent Edge-powered Data Reduction: A Systematic Literature Review

Published:2024-04-25 Issue:9 Volume:56 Page:1-39
ISSN:0360-0300
Container-title:ACM Computing Surveys
language:en
Short-container-title:ACM Comput. Surv.

Author:

Pioli Laércio¹^ORCID,de Macedo Douglas D. J.¹^ORCID,Costa Daniel G.²^ORCID,Dantas Mario A. R.³^ORCID

Affiliation:

1. Federal University of Santa Catarina, Florianopolis, Brazil

2. University of Porto, Porto, Portugal

3. Federal University of Juiz de Fora, Juiz de Fora, Brazil

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.

Funder

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior–Brasil (CAPES)–Finance Code 001

Publisher

Association for Computing Machinery (ACM)

Link

https://dl.acm.org/doi/pdf/10.1145/3656338

Reference108 articles.

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