Orchestrating the Development Lifecycle of Machine Learning-Based IoT Applications: A Taxonomy and Survey
It addresses the integration challenges for developers and researchers in ML-based IoT systems, but is incremental as it surveys existing methods rather than introducing new ones.
This paper tackles the complexity of integrating machine learning pipelines into IoT applications by providing a comprehensive survey and taxonomy of the development lifecycle, outlining core roadmaps and comparing existing techniques.
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.