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Jarvis: Moving Towards a Smarter Internet of Things

机译：贾维斯：走向更聪明的东西

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The deployment of Internet of Things (IoT) combined with cyber-physical systems is resulting in complex environments comprising of various devices interacting with each other and with users through apps running on computing platforms like mobile phones, tablets, and desktops. In addition, the rapid advances in Artificial Intelligence are making those devices able to autonomously modify their behaviors through the use of techniques such as reinforcement learning (RL). It is clear however that ensuring safety and security in such environments is critical. In this paper, we introduce Jarvis, a constrained RL framework for IoT environments that determines optimal devices actions with respect to user-defined goals, such as energy optimization, while at the same time ensuring safety and security. Jarvis is scalable and context independent in that it is applicable to any IoT environment with minimum human effort. We instantiate Jarvis for a smart home environment and evaluate its performance using both simulated and real world data. In terms of safety and security, Jarvis is able to detect 100% of the 214 manually crafted security violations collected from prior work and is able to correctly filter 99.2% of the user-defined benign anomalies and malfunctions from safety violations. For measuring functionality benefits, Jarvis is evaluated using real world smart home datasets with respect to three user required functionalities: energy use minimization, energy cost minimization, and temperature optimization. Our analysis shows that Jarvis provides significant advantages over normal device behavior in terms of functionality and over general unconstrained RL frameworks in terms of safety and security.
机译：事物互联网（物联网）与网络物理系统组合的部署导致复杂的环境，包括各种设备，各种设备彼此交互，并通过用户通过在计算平台上运行的应用程序，如移动电话，平板电脑和桌面上运行的应用程序。此外，人工智能的快速进步正在使这些设备能够通过使用诸如强化学习（RL）的技术来自主修改其行为。然而，很明显，确保这种环境中的安全性和安全性至关重要。在本文中，我们介绍了Jarvis，一个受限环境的约束RL框架，用于确定关于用户定义目标的最佳设备的动作，例如能量优化，同时确保安全性和安全性。 Jarvis是可扩展的和上下文独立的，因为它适用于任何具有最低人类努力的IOT环境。我们实例化Jarvis进行智能家庭环境，并使用模拟和现实世界数据进行评估其性能。在安全和安全性方面，Jarvis能够检测从事事先工作收集的214个手动制作的安全违规行为的100％，并且能够正确过滤99.2％的用户定义的良性异常和从安全违规行动的故障。为了测量功能效益，使用真实世界智能家庭数据集来评估jarvis，了解三个用户所需功能：能量使用最小化，能源成本最小化和温度优化。我们的分析表明，在安全性和安全性方面，JARVIS在功能方面和过度无拘无束的RL框架方面提供了显着的优势。

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