An Effective Machine Learning Based Algorithm for Inferring User Activities From IoT Device Events
An Effective Machine Learning Based Algorithm for Inferring User Activities From IoT Device Events
复制标题
DOI：
10.1109/jsac.2022.3191123
复制
发表时间：
2022-09
影响因子：
16.4
通讯作者：
G. Xue;Yinxin Wan;Xuanli Lin;Kuai Xu;Feng Wang
中科院分区：
文献类型：
--
作者：
G. Xue;Yinxin Wan;Xuanli Lin;Kuai Xu;Feng Wang
物联网(IoT)在智能家居中的快速和无处不在的部署创造了前所未有的机会，可以自动提取环境知识、意识和智能。许多现有的研究已经采用了机器学习方法或确定性方法来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。本文通过从设备事件序列中确定性地提取少量具有代表性的用户活动模式，然后应用无监督学习来计算这些用户活动模式的最优子集来推断用户活动模式，来研究从设备事件序列中推断用户活动模式的问题。基于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的大量实验，我们证明了我们的方案对设备故障和瞬时故障/延迟具有弹性，并且性能优于最先进的解决方案。
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.