Qian, B, Su, J, Wen, Z et al. (11 more authors) (2020) Orchestrating the Development Lifecycle of Machine Learning-based IoT Applications: A Taxonomy and Survey. ACM Computing Surveys, 53 (4). 82. ISSN 0360-0300
Abstract
Machine Learning (ML) and Internet of Things (IoT) are complementary advances: ML techniques unlock the potential of IoT with intelligence, and IoT applications increasingly feed data collected by sensors into ML models, thereby employing results to improve their business processes and services. Hence, orchestrating ML pipelines that encompass model training and implication involved in the holistic development lifecycle of an IoT application often leads to complex system integration. This article provides a comprehensive and systematic survey of the development lifecycle of ML-based IoT applications. We outline the core roadmap and taxonomy and subsequently assess and compare existing standard techniques used at individual stages.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2020 ACM. This is an author produced version of an article published in ACM Computing Surveys. Uploaded in accordance with the publisher's self-archiving policy. |
Dates: |
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Institution: | The University of Leeds |
Academic Units: | The University of Leeds > Faculty of Engineering & Physical Sciences (Leeds) > School of Computing (Leeds) |
Depositing User: | Symplectic Publications |
Date Deposited: | 22 Oct 2020 13:29 |
Last Modified: | 24 May 2021 13:00 |
Status: | Published |
Publisher: | Association for Computing Machinery (ACM) |
Identification Number: | 10.1145/3398020 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:166897 |