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Orchestrating the Development Lifecycle of Machine Learning-based IoT Applications: A Taxonomy and Survey

Lookup NU author(s): Bin Qian, Dr Jie Su, Dr Zhenyu Wen, Devki Jha, Yinhao Li, Dr Yu GuanORCiD, Dr Deepak PuthalORCiD, Professor Philip James, Professor Maciej KoutnyORCiD, Professor Raj Ranjan

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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.

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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.

Publication metadata

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

Year: 2020

Volume: 53

Issue: 4

Pages: 1-47

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

Publisher: ACM

URL: https://dl.acm.org/doi/10.1145/3398020

DOI: 10.1145/3398020

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