智能边缘数据还原:系统性文献综述

IF 23.8 1区 计算机科学 Q1 COMPUTER SCIENCE, THEORY & METHODS
Laércio Pioli Júnior, Douglas D. J. de Macedo, Daniel G. Costa, Mario A. R. Dantas
{"title":"智能边缘数据还原:系统性文献综述","authors":"Laércio Pioli Júnior, Douglas D. J. de Macedo, Daniel G. Costa, Mario A. R. Dantas","doi":"10.1145/3656338","DOIUrl":null,"url":null,"abstract":"<p>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 towards reducing sensed data volume. AI for DR on the edge is investigated in this study in the form of an Systematic Literature Review (slr) encompassing major issues such as data heterogeneity, 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.</p>","PeriodicalId":50926,"journal":{"name":"ACM Computing Surveys","volume":null,"pages":null},"PeriodicalIF":23.8000,"publicationDate":"2024-04-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Intelligent Edge-powered Data Reduction: A Systematic Literature Review\",\"authors\":\"Laércio Pioli Júnior, Douglas D. J. de Macedo, Daniel G. Costa, Mario A. R. Dantas\",\"doi\":\"10.1145/3656338\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p>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 towards reducing sensed data volume. AI for DR on the edge is investigated in this study in the form of an Systematic Literature Review (slr) encompassing major issues such as data heterogeneity, 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.</p>\",\"PeriodicalId\":50926,\"journal\":{\"name\":\"ACM Computing Surveys\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":23.8000,\"publicationDate\":\"2024-04-04\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"ACM Computing Surveys\",\"FirstCategoryId\":\"94\",\"ListUrlMain\":\"https://doi.org/10.1145/3656338\",\"RegionNum\":1,\"RegionCategory\":\"计算机科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"COMPUTER SCIENCE, THEORY & METHODS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"ACM Computing Surveys","FirstCategoryId":"94","ListUrlMain":"https://doi.org/10.1145/3656338","RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"COMPUTER SCIENCE, THEORY & METHODS","Score":null,"Total":0}
引用次数: 0

摘要

物联网(IoT)模式的发展及其作为一种经济实惠的数据源的大幅普及,给追求高效的数据收集、分发和存储带来了许多挑战。由于这种分层逻辑架构在很多情况下效率低下、成本高昂,因此出现了数据还原(DR)解决方案,以便在实际传输之前对数据进行预处理。为了提高 DR 性能,研究人员正在使用人工智能(AI)技术和模型来减少感知数据量。本研究通过系统文献综述(SLR)的形式,对用于边缘灾难恢复的人工智能进行了研究,其中包括数据异构性、基于人工智能的数据减少技术、架构和使用环境等主要问题。进行系统文献综述的目的是了解该领域的最新进展,突出最常见的挑战和潜在的研究趋势,以及建议的分类方法。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Intelligent Edge-powered Data Reduction: A Systematic Literature Review

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 towards reducing sensed data volume. AI for DR on the edge is investigated in this study in the form of an Systematic Literature Review (slr) encompassing major issues such as data heterogeneity, 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.

求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
ACM Computing Surveys
ACM Computing Surveys 工程技术-计算机:理论方法
CiteScore
33.20
自引率
0.60%
发文量
372
审稿时长
12 months
期刊介绍: ACM Computing Surveys is an academic journal that focuses on publishing surveys and tutorials on various areas of computing research and practice. The journal aims to provide comprehensive and easily understandable articles that guide readers through the literature and help them understand topics outside their specialties. In terms of impact, CSUR has a high reputation with a 2022 Impact Factor of 16.6. It is ranked 3rd out of 111 journals in the field of Computer Science Theory & Methods. ACM Computing Surveys is indexed and abstracted in various services, including AI2 Semantic Scholar, Baidu, Clarivate/ISI: JCR, CNKI, DeepDyve, DTU, EBSCO: EDS/HOST, and IET Inspec, among others.
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信