rIoT: Enabling Seamless Context-Aware Automation in the Internet of Things
rIoT: Enabling Seamless Context-Aware Automation in the Internet of Things
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DOI：
10.1109/mass.2019.00035
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发表时间：
2019-11
期刊：
影响因子：
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通讯作者：
Jie Hua;Chenguang Liu;T. Kalbarczyk;Catherine Wright;G. Roman;C. Julien
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文献类型：
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作者：
Jie Hua;Chenguang Liu;T. Kalbarczyk;Catherine Wright;G. Roman;C. Julien
移动的计算能力的进步和物联网（IoT）设备数量的增加丰富了IoT的可能性，但也增加了IoT用户所需的认知负荷。现有的上下文感知系统在物联网中提供各种级别的自动化。这些系统中的许多系统基于先验假设自适应地做出关于如何提供服务的决策。这些方法很难根据个人的动态环境进行个性化定制，因此当今的智能物联网空间通常需要与用户进行复杂和专业的交互，以便提供定制服务。我们提出了rIoT，这是一个物联网中人与设备交互的无缝和个性化自动化框架。rIoT利用现有技术跨异构设备和网络运行，为物联网中的设备交互提供一站式解决方案。我们展示了rIoT如何利用上下文之间的相似性，并采用类似决策树的方法来自适应地从与IoT空间的少量交互中捕获用户的偏好。我们在两个真实的数据集和一个真实的移动终端上测量了rIoT的性能，并与两种最先进的机器学习算法进行了比较。
Advances in mobile computing capabilities and an increasing number of Internet of Things (IoT) devices have enriched the possibilities of the IoT but have also increased the cognitive load required of IoT users. Existing context-aware systems provide various levels of automation in the IoT. Many of these systems adaptively take decisions on how to provide services based on assumptions made a priori. The approaches are difficult to personalize to an individual's dynamic environment, and thus today's smart IoT spaces often demand complex and specialized interactions with the user in order to provide tailored services. We propose rIoT, a framework for seamless and personalized automation of human-device interaction in the IoT. rIoT leverages existing technologies to operate across heterogeneous devices and networks to provide a one-stop solution for device interaction in the IoT. We show how rIoT exploits similarities between contexts and employs a decision-tree like method to adaptively capture a user's preferences from a small number of interactions with the IoT space. We measure the performance of rIoT on two real-world data sets and a real mobile device in terms of accuracy, learning speed, and latency in comparison to two state-of-the-art machine learning algorithms.