Orchestrating the Development Lifecycle of Machine Learning-Based IoT Applications: A Taxonomy and Survey
Abstract
Machine Learning (ML) and Internet of Things (IoT) are complementary advances: ML techniques unlock complete potentials 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 encompasses model training and implication involved in holistic development lifecycle of an IoT application often leads to complex system integration. This paper provides a comprehensive and systematic survey on the development lifecycle of ML-based IoT application. We outline core roadmap and taxonomy, and subsequently assess and compare existing standard techniques used in individual stage.
Cite
@article{arxiv.1910.05433,
title = {Orchestrating the Development Lifecycle of Machine Learning-Based IoT Applications: A Taxonomy and Survey},
author = {Bin Qian and Jie Su and Zhenyu Wen and Devki Nandan Jha and Yinhao Li and Yu Guan and Deepak Puthal and Philip James and Renyu Yang and Albert Y. Zomaya and Omer Rana and Lizhe Wang and Maciej Koutny and Rajiv Ranjan},
journal= {arXiv preprint arXiv:1910.05433},
year = {2020}
}
Comments
50 pages, Accepted by ACM Computing Surveys (CSUR)