Aspect oriented context-aware and event-driven data processing for internet of things

Michal Trnka, J. Svacina, T. Cerný, Eunjee Song
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Abstract

The Internet of Things is currently getting significant interest from the scientific community. Academia and industry are both focused on moving ahead in attempts to put Internet of Things in practical use. Sensors and other devices in the Internet of Things networks generate tremendous amounts of data. Most of the times those data carry some contextual information and thus could be used for context-aware application. However, handling the vast amount of data becomes increasingly demanding task. In this article we propose event-driven solution for context-aware applications. In our method events are generated by Internet of Things devices and further propagated to subscribed actions. It support event filtering based on the data the event carries with him, like temperature or location. We demonstrate feasibility of our solution and compare it with traditional approach.
面向方面、上下文感知和事件驱动的物联网数据处理
物联网目前正引起科学界的极大兴趣。学术界和工业界都在努力推进物联网的实际应用。物联网网络中的传感器和其他设备会产生大量数据。大多数情况下,这些数据携带一些上下文信息,因此可以用于上下文感知应用程序。然而,处理大量数据的任务变得越来越艰巨。在本文中,我们为上下文感知应用程序提出了事件驱动的解决方案。在我们的方法中,事件由物联网设备生成,并进一步传播到订阅的操作。它支持基于事件携带的数据(如温度或位置)进行事件过滤。验证了该方法的可行性,并与传统方法进行了比较。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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