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Machine Learning in Real-Time Internet of Things (IoT) Systems: A Survey
Bian, Jiang1; Arafat, Abdullah Al2; Xiong, Haoyi3; Li, Jing4; Li, Li5; Chen, Hongyang6; Wang, Jun2; Dou, Dejing3; Guo, Zhishan2
2022
Source PublicationIEEE Internet of Things Journal
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 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 community address the strict timing requirements of ML/DL technologies to include in real-time systems. This paper will rigorously explore the state-of-the-art results emphasizing the strengths and weaknesses in ML/DL-based scheduling techniques, accuracy vs. execution time trade-off policies of ML algorithms, and security & privacy of learning-based algorithms in real-time IoT systems.

KeywordDeep Learning Hardware Internet Of Things Internet Of Things Machine Learning Machine Learning Algorithms Real-time Systems Real-time Systems. Scheduling Scheduling Algorithms Sensors Task Analysis
DOI10.1109/JIOT.2022.3161050
URLView the original
Indexed BySCIE
Language英語English
WOS Research AreaComputer Science ; Engineering ; Telecommunications
WOS SubjectComputer Science, Information Systems ; Engineering, Electrical & Electronic ; Telecommunications
WOS IDWOS:000800215600039
Scopus ID2-s2.0-85127082088
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Cited Times [WOS]:0   [WOS Record]     [Related Records in WOS]
Document TypeJournal article
CollectionUniversity of Macau
Corresponding AuthorXiong, Haoyi; Guo, Zhishan
Affiliation1.Department of Electrical and Computer Engineering, University of Central Florida, Orlando, FL, USA, and also with the Big Data Lab, Baidu Inc., Beijing, China.
2.Department of Electrical and Computer Engineering, University of Central Florida, Orlando, FL, USA.
3.Big Data Lab, Baidu Inc., Beijing, China.
4.Department of Computer Science, New Jersey Institute of Technology, Newark, NJ, USA.
5.Department of Computer and Information Science, University of Macau, Tapia, Macao.
6.Research Center for Intelligent Network, Zhejiang Lab, Hangzhou, Zhejiang, China.
Recommended Citation
GB/T 7714
Bian, Jiang,Arafat, Abdullah Al,Xiong, Haoyi,et al. Machine Learning in Real-Time Internet of Things (IoT) Systems: A Survey[J]. IEEE Internet of Things Journal,2022.
APA Bian, Jiang,Arafat, Abdullah Al,Xiong, Haoyi,Li, Jing,Li, Li,Chen, Hongyang,Wang, Jun,Dou, Dejing,&Guo, Zhishan.(2022).Machine Learning in Real-Time Internet of Things (IoT) Systems: A Survey.IEEE Internet of Things Journal.
MLA Bian, Jiang,et al."Machine Learning in Real-Time Internet of Things (IoT) Systems: A Survey".IEEE Internet of Things Journal (2022).
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