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This is the authors' accepted manuscript of an article that has been published in its final definitive form by ACM, 2020.
For re-use rights please refer to the publisher's terms and conditions.
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.
Author(s): Qian B, Jie S, Wen Z, Jha D, Li Y, Guan Y, Puthal D, James P, Yang R, Zomaya A, Rana O, Wang L, Koutny M, Ranjan R
Publication type: Article
Publication status: Published
Journal: ACM Computing Surveys
Print publication date: 01/09/2020
Online publication date: 03/08/2020
Acceptance date: 01/05/2020
Date deposited: 10/11/2020
ISSN (print): 0360-0300
ISSN (electronic): 1557-7341
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