Won-Hee Han, Sung-Won Kim, Sun-Mi Park, Chang-Wu Lee, J. Park, Y. Jeong
{"title":"基于上下文感知的大规模USN中间件","authors":"Won-Hee Han, Sung-Won Kim, Sun-Mi Park, Chang-Wu Lee, J. Park, Y. Jeong","doi":"10.1109/EUC.2008.211","DOIUrl":null,"url":null,"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.","PeriodicalId":430277,"journal":{"name":"2008 IEEE/IFIP International Conference on Embedded and Ubiquitous Computing","volume":"18 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2008-12-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":"{\"title\":\"Large-Scale USN Middleware Based on Context-Aware\",\"authors\":\"Won-Hee Han, Sung-Won Kim, Sun-Mi Park, Chang-Wu Lee, J. Park, Y. Jeong\",\"doi\":\"10.1109/EUC.2008.211\",\"DOIUrl\":null,\"url\":null,\"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.\",\"PeriodicalId\":430277,\"journal\":{\"name\":\"2008 IEEE/IFIP International Conference on Embedded and Ubiquitous Computing\",\"volume\":\"18 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2008-12-17\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"2\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2008 IEEE/IFIP International Conference on Embedded and Ubiquitous Computing\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/EUC.2008.211\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2008 IEEE/IFIP International Conference on Embedded and Ubiquitous Computing","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/EUC.2008.211","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
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.