Orchestrating the development lifecycle of machine learning-based IoT applications

Qian, Bin, Su, Jie, Wen, Zhenyu, Jha, Devki Nandan, Li, Yinhao, Guan, Yu, Puthal, Deepak, James, Philip, Yang, Renyu, Zomaya, Albert Y., Rana, Omer ORCID: https://orcid.org/0000-0003-3597-2646, Wang, Lizhe, Koutny, Maciej and Ranjan, Rajiv 2020. Orchestrating the development lifecycle of machine learning-based IoT applications. ACM Computing Surveys 53 (4) , 82. 10.1145/3398020

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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.

Item Type:	Article
Date Type:	Publication
Status:	Published
Schools:	Computer Science & Informatics
Publisher:	Association for Computing Machinery (ACM)
ISSN:	0360-0300
Last Modified:	09 Nov 2022 09:34
URI:	https://orca.cardiff.ac.uk/id/eprint/136170

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