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

Bin Qian, Jie Su, Zhenyu Wen, Devki Nandan Jha, Yinhao Li, Yu Guan, Deepak Puthal, Philip James, Renyu Yang, Albert Y. Zomaya, Omer Rana, Lizhe Wang, MacIej Koutny, Rajiv Ranjan

Research output: Contribution to journalArticlepeer-review

55 Scopus citations

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.

Original languageBritish English
Article number3398020
JournalACM Computing Surveys
Volume53
Issue number4
DOIs
StatePublished - Sep 2020

Keywords

  • deep learning
  • IoT
  • machine learning
  • orchestration

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