Machine Learning in Real-Time Internet of Things (IoT) Systems: A Survey

Jiang Bian, Abdullah Al Arafat, Haoyi Xiong, Jing Li, Li Li, Hongyang Chen, Jun Wang, Dejing Dou, Zhishan Guo

Research output: Contribution to journalReview articlepeer-review

15 Scopus citations


Over the last decade, machine learning (ML) and deep learning (DL) algorithms have significantly evolved and been employed in diverse applications, such as computer vision, natural language processing, automated speech recognition, etc. Real-time safety-critical embedded and Internet of Things (IoT) systems, such as autonomous driving systems, UAVs, drones, security robots, etc., heavily rely on ML/DL-based technologies, accelerated with the improvement of hardware technologies. The cost of a deadline (required time constraint) missed by ML/DL algorithms would be catastrophic in these safety-critical systems. However, ML/DL algorithm-based applications have more concerns about accuracy than strict time requirements. Accordingly, researchers from the real-time systems (RTSs) community address the strict timing requirements of ML/DL technologies to include in RTSs. This article will rigorously explore the state-of-the-art results emphasizing the strengths and weaknesses in ML/DL-based scheduling techniques, accuracy versus execution time tradeoff policies of ML algorithms, and security and privacy of learning-based algorithms in real-time IoT systems.

Original languageEnglish (US)
Pages (from-to)8364-8386
Number of pages23
JournalIEEE Internet of Things Journal
Issue number11
StatePublished - Jun 1 2022
Externally publishedYes

All Science Journal Classification (ASJC) codes

  • Signal Processing
  • Information Systems
  • Hardware and Architecture
  • Computer Science Applications
  • Computer Networks and Communications


  • Deep learning (DL)
  • Internet of Things (IoT)
  • Machine learning (ML)
  • Real-time systems (RTSs)
  • Scheduling


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