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

本研究项目的目标是设计和开发一个高效、健壮的RFID流处理系统，以应对新兴RFID部署中的挑战，包括数据信息不匹配、数据不完整和噪声以及数据量大，并实现实时跟踪和监控。该项目有两个主要贡献。第一个贡献是在RFID流上的低级别解释和压缩底层。这种基片提供对不完整和有噪声的原始数据的准确解释；它使用概率算法推断未观察到的对象的位置和对象之间的关系。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中持续评估这些模式来解决数据信息不匹配问题。该项目提供了复杂事件处理的基础，包括紧凑、富有表现力的事件语言、理论基础、基于自动机的机制，用于对事件流进行有效的模式评估，以及用于对由低级解释和压缩产生的事件流进行健壮处理的技术。该项目通过课程开发和教学与研究实验室的开发将研究和教育结合在一起，并通过大学外展和CRA？S分布式导师计划使妇女和少数民族更广泛地参与研究。该项目将产生更广泛的影响，包括通过该项目的网站(http://rfid-streams.cs.umass.edu/)和技术转让)向研究社区发布源代码、模拟器、数据集和基准，以及在供应链管理、医疗保健、制药、图书馆管理等方面的潜在应用。

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.