为下一代智能可穿戴系统整合机器学习和边缘计算特刊

IF 6.2 2区 计算机科学 Q1 COMPUTER SCIENCE, THEORY & METHODS Future Generation Computer Systems-The International Journal of Escience Pub Date : 2024-10-30 DOI:10.1016/j.future.2024.107574
Paolo Gastaldo , Edoardo Ragusa , Strahinja Dosen , Francesco Palmieri
{"title":"为下一代智能可穿戴系统整合机器学习和边缘计算特刊","authors":"Paolo Gastaldo ,&nbsp;Edoardo Ragusa ,&nbsp;Strahinja Dosen ,&nbsp;Francesco Palmieri","doi":"10.1016/j.future.2024.107574","DOIUrl":null,"url":null,"abstract":"<div><div>Machine learning (ML) provides an enabling technology for the development of the next generation of smart devices. However, the integration of ML and edge computing faces major challenges. While powerful models can tackle difficult tasks such as visual recognition or natural language processing, the constrained resources of embedded systems might prevent direct deployment of the designed inference function into an edge device. This Special Issue collects manuscripts describing methodologies and systems that tackle the integration of ML into embedded systems. The focus is on solutions that can stimulate significant improvements across different domains.</div></div>","PeriodicalId":55132,"journal":{"name":"Future Generation Computer Systems-The International Journal of Escience","volume":"164 ","pages":"Article 107574"},"PeriodicalIF":6.2000,"publicationDate":"2024-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Special Issue on integration of machine learning and edge computing for next generation of smart wearable systems\",\"authors\":\"Paolo Gastaldo ,&nbsp;Edoardo Ragusa ,&nbsp;Strahinja Dosen ,&nbsp;Francesco Palmieri\",\"doi\":\"10.1016/j.future.2024.107574\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><div>Machine learning (ML) provides an enabling technology for the development of the next generation of smart devices. However, the integration of ML and edge computing faces major challenges. While powerful models can tackle difficult tasks such as visual recognition or natural language processing, the constrained resources of embedded systems might prevent direct deployment of the designed inference function into an edge device. This Special Issue collects manuscripts describing methodologies and systems that tackle the integration of ML into embedded systems. The focus is on solutions that can stimulate significant improvements across different domains.</div></div>\",\"PeriodicalId\":55132,\"journal\":{\"name\":\"Future Generation Computer Systems-The International Journal of Escience\",\"volume\":\"164 \",\"pages\":\"Article 107574\"},\"PeriodicalIF\":6.2000,\"publicationDate\":\"2024-10-30\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Future Generation Computer Systems-The International Journal of Escience\",\"FirstCategoryId\":\"94\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S0167739X24005387\",\"RegionNum\":2,\"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":"Future Generation Computer Systems-The International Journal of Escience","FirstCategoryId":"94","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0167739X24005387","RegionNum":2,"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

机器学习(ML)为下一代智能设备的开发提供了有利技术。然而,ML 与边缘计算的整合面临着重大挑战。虽然强大的模型可以解决视觉识别或自然语言处理等困难任务,但嵌入式系统资源有限,可能无法将设计好的推理功能直接部署到边缘设备中。本特刊收集了介绍将 ML 集成到嵌入式系统的方法和系统的手稿。重点关注可促进不同领域显著改进的解决方案。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
Special Issue on integration of machine learning and edge computing for next generation of smart wearable systems
Machine learning (ML) provides an enabling technology for the development of the next generation of smart devices. However, the integration of ML and edge computing faces major challenges. While powerful models can tackle difficult tasks such as visual recognition or natural language processing, the constrained resources of embedded systems might prevent direct deployment of the designed inference function into an edge device. This Special Issue collects manuscripts describing methodologies and systems that tackle the integration of ML into embedded systems. The focus is on solutions that can stimulate significant improvements across different domains.
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
CiteScore
19.90
自引率
2.70%
发文量
376
审稿时长
10.6 months
期刊介绍: Computing infrastructures and systems are constantly evolving, resulting in increasingly complex and collaborative scientific applications. To cope with these advancements, there is a growing need for collaborative tools that can effectively map, control, and execute these applications. Furthermore, with the explosion of Big Data, there is a requirement for innovative methods and infrastructures to collect, analyze, and derive meaningful insights from the vast amount of data generated. This necessitates the integration of computational and storage capabilities, databases, sensors, and human collaboration. Future Generation Computer Systems aims to pioneer advancements in distributed systems, collaborative environments, high-performance computing, and Big Data analytics. It strives to stay at the forefront of developments in grids, clouds, and the Internet of Things (IoT) to effectively address the challenges posed by these wide-area, fully distributed sensing and computing systems.
期刊最新文献
Editorial Board AIHO: Enhancing task offloading and reducing latency in serverless multi-edge-to-cloud systems DSDM-TCSE: Deterministic storage and deletion mechanism for trusted cloud service environments Energy management in smart grids: An Edge-Cloud Continuum approach with Deep Q-learning Service migration with edge collaboration: Multi-agent deep reinforcement learning approach combined with user preference adaptation
×
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
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 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 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1