Large-Scale USN Middleware Based on Context-Aware

Won-Hee Han, Sung-Won Kim, Sun-Mi Park, Chang-Wu Lee, J. Park, Y. Jeong
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引用次数: 2

Abstract

This paper designs LS-ware (Large-Scale USN middleware) in order to collect and save large-scale sensing data and to analyze collected data and run status sensing function. LS-ware not only provides USN middlewarepsilas basic function such as Meta information, quality process, or status management, but also makes large-scale sensing data light-weighted and provides a suitable event processing feature. LS-ware develops and utilizes a four level scheduling algorithm for continuous large-scale sensing data collection. In order to manage sensing data more effectively, it parses data packet structure, extracts information, and looses datapsilas weight. It recognizes an event from light weighted sensing data, and sends the status information to a client.
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基于上下文感知的大规模USN中间件
本文设计了LS-ware (Large-Scale USN middleware),用于采集和保存大规模的感知数据,并对采集到的数据进行分析和运行状态感知功能。LS-ware不仅提供了USN中间件的Meta信息、质量处理或状态管理等基本功能,还使大规模传感数据轻量化,并提供了合适的事件处理功能。LS-ware开发并利用了一种四级调度算法,用于连续大规模的传感数据采集。为了更有效地管理传感数据,该算法对数据包结构进行解析,提取信息,降低数据权重。它从轻量级感知数据中识别事件,并将状态信息发送给客户端。
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