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

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

作者机构：Univ Cent Florida Dept Elect & Comp Engn Orlando FL 32816 USA Baidu Inc Big Data Lab Beijing 100193 Peoples R China New Jersey Inst Technol Dept Comp Sci Newark NJ 07102 USA Univ Macau Dept Comp & Informat Sci Macau Peoples R China Zhejiang Lab Res Ctr Intelligent Network Hangzhou 311121 Zhejiang Peoples R China 

出 版 物：《IEEE INTERNET OF THINGS JOURNAL》 (IEEE Internet Things J.)

年 卷 期：2022年第9卷第11期

页      面：8364-8386页

核心收录：

学科分类：0810[工学-信息与通信工程] 0808[工学-电气工程] 08[工学] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：National Science Foundation, USA [CNS-1948457, CNS-1850851, PPoSS-2028481, OIA-1937833] National Key Research and Development Program of China [2021ZD0110303] 

主　　题：Task analysis Real-time systems Machine learning algorithms Scheduling algorithms Internet of Things Hardware Sensors Deep learning (DL) Internet of Things (IoT) machine learning (ML) real-time systems (RTSs) scheduling 

摘      要：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.