"Machine Learning in Real-Time Internet of Things (IoT) Systems: A Surv" by Jiang Bian, Abdullah Al Arafat et al.
 

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

Document Type

Article

Publication Date

6-1-2022

Abstract

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.

Identifier

85127082088 (Scopus)

Publication Title

IEEE Internet of Things Journal

External Full Text Location

https://doi.org/10.1109/JIOT.2022.3161050

e-ISSN

23274662

First Page

8364

Last Page

8386

Issue

11

Volume

9

Grant

CNS 1850851

Fund Ref

National Science Foundation

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