Laércio Pioli Júnior, Douglas D. J. de Macedo, Daniel G. Costa, Mario A. R. Dantas
{"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}
引用次数: 0
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 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.
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。
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.
京公网安备 11010802042870号
京ICP备2023020795号-1
分享
求助内容：
应助结果提醒方式：
扫码关注我们
