首页> 外文会议>9th ACM/IEEE international conference on information processing in sensor networks 2010 >Poster Abstract: Discovering Routine Events in Sensor Streams for Macroscopic Sensing Composition
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Poster Abstract: Discovering Routine Events in Sensor Streams for Macroscopic Sensing Composition

机译:海报摘要:在传感器流中发现例行事件以进行宏观传感合成

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摘要

This poster abstract introduces the problem of macroscopic sensing composition, where a sensor capable to detect complex events is synthesized dynamically by a collection of simpler sensors using a data-driven approach. Our solution is geared towards discovering the structure of human activities by considering the triggering of simple sensors over a diverse set of spatial and temporal scales. The goal is to identify routines from their components by leveraging the fact that the components have the same temporal persistence as the routines themselves. To this end we have devised an algorithm for determining if an event occurs consistently within a time interval where the interval is periodic but the event is not. The goal of the algorithm is to identify events with this property and also determine the minimum interval in which they occur. Our first results using testbed data and simulations indicate that this approach can uncover components of routines by identifying which events are parts of the same routine through their temporal properties.
机译:该海报摘要介绍了宏观传感组成的问题,其中能够检测复杂事件的传感器是通过使用数据驱动的方法由一组较简单的传感器动态合成的。我们的解决方案旨在通过考虑在各种时空尺度上触发简单传感器来发现人类活动的结构。目的是通过利用组件具有与例程本身相同的时间持久性这一事实,从组件中识别例程。为此,我们设计了一种算法来确定事件是否在时间间隔内一致发生,该时间间隔是周期性的,但事件不是周期性的。该算法的目标是识别具有此属性的事件,并确定事件发生的最小间隔。我们使用测试床数据和模拟得出的第一个结果表明,该方法可以通过确定其事件的时间属性来确定同一事件的组成部分,从而发现该事件的组成部分。

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