Abstract

Integrating Internet of Things (IoT) sensor data from heterogeneous sources with domain knowledge and context information in real-time is a challenging task in IoT healthcare data management applications that can be solved with semantics. Existing IoT platforms often have issues with preserving the privacy of patient data. Moreover, configuring and managing context-aware stream processing queries in semantic IoT platforms requires much manual, labor-intensive effort. Generic queries can deal with context changes but often lead to performance issues caused by the need for expressive real-time semantic reasoning. In addition, query window parameters are part of the manual configuration and cannot be made context-dependent. To tackle these problems, this paper presents DIVIDE, a component for a semantic IoT platform that adaptively derives and manages the queries of the platform’s stream processing components in a context-aware and scalable manner, and that enables privacy by design. By performing semantic reasoning to derive the queries when context changes are observed, their real-time evaluation does require any reasoning. The results of an evaluation on a homecare monitoring use case demonstrate how activity detection queries derived with DIVIDE can be evaluated in on average less than 3.7 seconds and can therefore successfully run on low-end IoT devices.

中文翻译：

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通过 DIVIDE 实现 IoT 数据流的上下文感知查询派生，通过设计实现隐私

摘要

将来自异构源的物联网 (IoT) 传感器数据与领域知识和上下文信息实时集成是物联网医疗保健数据管理应用程序中的一项具有挑战性的任务，可以通过语义来解决。现有的物联网平台通常在保护患者数据隐私方面存在问题。此外，在语义 IoT 平台中配置和管理上下文感知流处理查询需要大量人工、劳动密集型工作。通用查询可以处理上下文变化，但通常会因需要表达实时语义推理而导致性能问题。此外，查询窗口参数是手动配置的一部分，不能与上下文相关。为了解决这些问题，本文提出了 DIVIDE，语义物联网平台的组件，以上下文感知和可扩展的方式自适应地派生和管理平台流处理组件的查询，并通过设计实现隐私。通过在观察到上下文变化时执行语义推理来导出查询，它们的实时评估确实需要任何推理。对家庭护理监控用例的评估结果表明，使用 DIVIDE 派生的活动检测查询可以在平均不到 3.7 秒的时间内完成评估，因此可以在低端物联网设备上成功运行。他们的实时评估确实需要任何推理。对家庭护理监控用例的评估结果表明，使用 DIVIDE 派生的活动检测查询可以在平均不到 3.7 秒的时间内完成评估，因此可以在低端物联网设备上成功运行。他们的实时评估确实需要任何推理。对家庭护理监控用例的评估结果表明，使用 DIVIDE 派生的活动检测查询可以在平均不到 3.7 秒的时间内完成评估，因此可以在低端物联网设备上成功运行。