In the context of smart home, it is very important to identify usage patterns of Internet of things (IoT) devices. Finding these patterns and using them for decision-making can provide ease, comfort, practicality, and autonomy when executing daily activities. Performing knowledge extraction in a decentralized approach is a computational challenge considering the tight storage and processing constraints of IoT devices, unlike deep learning, which demands a massive amount of data, memory, and processing capability. This article describes a method for mining implicit correlations among the actions of IoT devices through embedded associative analysis. Based on support, confidence, and lift metrics, our proposed method identifies the most relevant correlations between a pair of actions of different IoT devices and suggests the integration between them through hypertext transfer protocol requests. We have compared our proposed method with a centralized method. Experimental results show that the most relevant rules for both methods are the same in 99.75% of cases. Moreover, our proposed method was able to identify relevant correlations that were not identified by the centralized one. Thus, we show that associative analysis of IoT device state change is efficient to provide an intelligent and highly integrated IoT platform while avoiding the single point of failure problem.

中文翻译：

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DARE：分布式物联网设备中嵌入式数据集的去中心化关联规则提取方案

在智能家居的背景下，识别物联网 (IoT) 设备的使用模式非常重要。在执行日常活动时，找到这些模式并将其用于决策可以提供轻松、舒适、实用性和自主性。与需要大量数据、内存和处理能力的深度学习不同，考虑到物联网设备的严格存储和处理限制，以分散的方法执行知识提取是一项计算挑战。本文介绍了一种通过嵌入式关联分析挖掘物联网设备动作之间隐含关联的方法。根据支持度、置信度和提升度指标，我们提出的方法确定了不同物联网设备的一对动作之间最相关的相关性，并通过超文本传输​​协议请求建议它们之间的集成。我们将我们提出的方法与集中式方法进行了比较。实验结果表明，两种方法最相关的规则在 99.75% 的情况下是相同的。此外，我们提出的方法能够识别集中式方法未识别的相关相关性。因此，我们表明物联网设备状态变化的关联分析可有效提供智能且高度集成的物联网平台，同时避免单点故障问题。实验结果表明，两种方法最相关的规则在 99.75% 的情况下是相同的。此外，我们提出的方法能够识别集中式方法未识别的相关相关性。因此，我们表明物联网设备状态变化的关联分析可有效提供智能且高度集成的物联网平台，同时避免单点故障问题。实验结果表明，两种方法最相关的规则在 99.75% 的情况下是相同的。此外，我们提出的方法能够识别集中式方法未识别的相关相关性。因此，我们表明物联网设备状态变化的关联分析可有效提供智能且高度集成的物联网平台，同时避免单点故障问题。