A Dataflow-Oriented Approach for Machine-Learning-Powered Internet of Things Applications Articles uri icon

authors

  • Baldoni, Gabriele
  • TEIXEIRA, RAFAEL
  • MAGALHAES GUIMARAES, CARLOS EDUARDO
  • ANTUNES, MARIO
  • GOMES, DIOGO
  • CORSARO, ANGELO

publication date

  • September 2023

start page

  • 1

end page

  • 15

issue

  • 18, 3940

volume

  • 12

International Standard Serial Number (ISSN)

  • 2079-9292

abstract

  • The rise of the Internet of Things (IoT) has led to an exponential increase in data generated by connected devices. Machine Learning (ML) has emerged as a powerful tool to analyze these data and enable intelligent IoT applications. However, developing and managing ML applications in the decentralized Cloud-to-Things continuum is extremely complex. This paper proposes Zenoh-Flow, a dataflow programming framework that supports the implementation of End-to-End (E2E) ML pipelines in a fully decentralized manner and abstracted from communication aspects. Thus, it simplifies the development and upgrade process of the next-generation ML-powered applications in the IoT domain. The proposed framework was demonstrated using a real-world use case, and the results showcased a significant improvement in overall performance and network usage compared to the original implementation. Additionally, other of its inherent benefits are a significant step towards developing efficient and scalable ML applications in the decentralized IoT ecosystem.

subjects

  • Telecommunications

keywords

  • iot; dataflow programming; machine learning; ml ops