Machine learning and data analytics for the IoT

Erwin Adi, Adnan Anwar, Zubair Baig, Sherali Zeadally

Research output: Contribution to journalArticlepeer-review

124 Scopus citations


The Internet of Things (IoT) applications have grown in exorbitant numbers, generating a large amount of data required for intelligent data processing. However, the varying IoT infrastructures (i.e., cloud, edge, fog) and the limitations of the IoT application layer protocols in transmitting/receiving messages become the barriers in creating intelligent IoT applications. These barriers prevent current intelligent IoT applications to adaptively learn from other IoT applications. In this paper, we critically review how IoT-generated data are processed for machine learning analysis and highlight the current challenges in furthering intelligent solutions in the IoT environment. Furthermore, we propose a framework to enable IoT applications to adaptively learn from other IoT applications and present a case study in how the framework can be applied to the real studies in the literature. Finally, we discuss the key factors that have an impact on future intelligent applications for the IoT.

Original languageEnglish
Pages (from-to)16205-16233
Number of pages29
JournalNeural Computing and Applications
Issue number20
StatePublished - Oct 1 2020

Bibliographical note

Publisher Copyright:
© 2020, Springer-Verlag London Ltd., part of Springer Nature.


  • Cybersecurity
  • Intelligent systems
  • Internet of Things
  • Machine learning

ASJC Scopus subject areas

  • Software
  • Artificial Intelligence


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