CAREER: Efficient, Robust RFID Stream Processing for Tracking and Monitoring

职业：用于跟踪和监控的高效、稳健的 RFID 流处理

• 批准号: 0746939
• 负责人: Yanlei Diao
• 金额: $ 58.89万
• 依托单位: University of Massachusetts Amherst
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2008
• 资助国家: 美国
• 起止时间: 2008-06-15 至 2014-05-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0746939&HistoricalAwards=false
• 关键词: CAREER; Efficient; Robust; RFID; Stream

The goal of this research project is to design and develop an efficient, robust RFID stream processing system that addresses the challenges in emerging RFID deployments, including the data-information mismatch, incomplete and noisy data, and high data volume, and enables real-time tracking and monitoring. This project has two main contributions. The first contribution is a low-level interpretation and compression substrate over RFID streams. This substrate offers accurate interpretation of incomplete and noisy raw data; it infers locations of unobserved objects and inter-object relationships using probabilistic algorithms. To handle high data volume, it performs online interpretation, enabling online compression by identifying and discarding redundant data. The second contribution is higher-level complex event processing that addresses the data-information mismatch by encoding application information needs as event patterns and evaluating these patterns continuously over event streams. This project offers a foundation for complex event processing with a compact, expressive event language, theoretical underpinnings, automata-based mechanisms for efficient pattern evaluation over event streams, and techniques for robust processing over event streams that result from low-level interpretation and compression. This project integrates research and education through curriculum development and teaching and research lab development, and enables broader participation of women and minorities in research through college outreach and CRA?s distributed mentor program. This project will have broader impacts including release of source code, simulators, datasets, and benchmarks to the research community via the project's Web site (http://rfid-streams.cs.umass.edu/) and technology transfer with potential applications in supply chain management, healthcare, pharmaceuticals, library management, etc.

该研究项目的目标是设计和开发一个高效，强大的RFID流处理系统，以解决新兴RFID部署中的挑战，包括数据信息不匹配，不完整和噪声数据，以及高数据量，并实现实时跟踪和监控。该项目有两个主要贡献。第一个贡献是一个低层次的解释和压缩基板上的RFID流。该基板提供了不完整的和嘈杂的原始数据的准确解释;它推断未观察到的对象和对象间的关系，使用概率算法的位置。为了处理大数据量，它执行在线解释，通过识别和丢弃冗余数据来实现在线压缩。第二个贡献是更高级别的复杂事件处理，它通过将应用程序信息需求编码为事件模式并在事件流上连续评估这些模式来解决数据信息不匹配问题。该项目提供了一个紧凑的，富有表现力的事件语言，理论基础，基于自动机的机制，有效的模式评估的事件流，和技术，强大的处理事件流，从低层次的解释和压缩的复杂事件处理的基础。该项目通过课程开发、教学和研究实验室开发，将研究和教育结合起来，并通过大学外联和CRA，使妇女和少数民族能够更广泛地参与研究。的分布式导师计划。该项目将产生更广泛的影响，包括通过项目网站（http：//rfid-streams.cs.umass.edu/）向研究界发布源代码、模拟器、数据集和基准，以及在供应链管理、医疗保健、制药、图书馆管理等方面具有潜在应用的技术转让。