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.

中文翻译：

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实时物联网 (IoT) 系统中的机器学习：调查


在过去的十年中，机器学习 (ML) 和深度学习 (DL) 算法取得了显着的发展，并被应用于多种应用，例如计算机视觉、自然语言处理、自动语音识别等。物联网（IoT）系统，例如自动驾驶系统、无人机、无人机、安全机器人等，严重依赖基于ML/DL的技术，并随着硬件技术的改进而加速。在这些安全关键系统中，机器学习/深度学习算法错过最后期限（所需的时间限制）的成本将是灾难性的。然而，基于 ML/DL 算法的应用程序更关心准确性，而不是严格的时间要求。因此，实时系统 (RTS) 社区的研究人员解决了将 ML/DL 技术纳入 RTS 中的严格时序要求。本文将严格探讨最先进的结果，强调基于 ML/DL 的调度技术的优点和缺点、ML 算法的准确性与执行时间权衡策略，以及基于学习的算法的安全性和隐私性。时间物联网系统。