The rapid and ubiquitous deployment of Internet of Things (IoT) in smart homes has created unprecedented opportunities to automatically extract environmental knowledge, awareness, and intelligence. Many existing studies have adopted either machine learning approaches or deterministic approaches to infer IoT device events and/or user activities from network traffic in smart homes. In this paper, we study the problem of inferring user activity patterns from a sequence of device events by first deterministically extracting a small number of representative user activity patterns from the sequence of device events, then applying unsupervised learning to compute an optimal subset of these user activity patterns to infer user activity patterns. Based on extensive experiments with sequences of device events triggered by 2,959 real user activities and up to 30,000 synthetic user activities, we demonstrate that our scheme is resilient to device malfunctions and transient failures/delays, and outperforms the state-of-the-art solution.

中文翻译：

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一种基于机器学习的有效算法，用于从物联网设备事件推断用户活动


物联网 (IoT) 在智能家居中的快速、无处不在的部署为自动提取环境知识、意识和智能创造了前所未有的机会。许多现有研究采用机器学习方法或确定性方法从智能家居中的网络流量推断物联网设备事件和/或用户活动。在本文中，我们研究了从设备事件序列推断用户活动模式的问题，首先从设备事件序列中确定性地提取少量代表性用户活动模式，然后应用无监督学习来计算这些用户的最佳子集活动模式来推断用户活动模式。基于对 2,959 个真实用户活动和多达 30,000 个合成用户活动触发的设备事件序列进行的广泛实验，我们证明了我们的方案能够适应设备故障和瞬时故障/延迟，并且优于最先进的解决方案。