Efficient, Robust RFID Stream Processing for Tracking and Monitoring

University of Massachusetts, Amherst

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Recent advances in Radio Frequency Identification (RFID) technology and ubiquitous networking are facilitating the emergence of an information infrastructure that collects real-time data associated with physical objects and delivers high-value content to a variety of user communities. Emerging user communities include supply chain management, healthcare, postal services, to name just a new.

Data stream management is central to such an RFID-based information infrastructure---it allows the relevant information to be sifted out of the flood of RFID data immediately after it emerges. Despite recent advances in related areas such as relational stream processing and sensor data management, RFID data---inherently noisy data for identification of individual objects---raises many new questions. The significant mismatch between raw RFID data and meaningful, actionable information required by RFID applications requires complex processing beyond the capabilities of existing stream systems. The incomplete and noisy nature of RFID data further complicates such data-information translation. The volumes of data generated from large RFID deployments can also stress or overwhelm existing stream systems.

The goal of this research project is to design and develop an efficient, robust RFID stream processing system that addresses the challenges posed by the data-information mismatch, incomplete and noisy data, and high data volume, and enables real-time tracking and monitoring. This project has two main components.

• SPIRE: inference and compression over RFID streams . This low-level substrate offers accurate interpretation of incomplete and noisy raw data. In particular, 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.
• SASE: complex event processing. This higher-level event processing system addresses the data-information mismatch by encoding application information needs as event patterns and evaluating these patterns continuously over event streams. The SASE system offers a compact, expressive event language, automata-based mechanisms for efficient event pattern evaluation, and advanced techniques for robust event processing.

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Project Members

Team at UMass Amherst

• Yanlei Diao (faculty)
• Neil Immerman (faculty)
• Prashant Shenoy (faculty)
• Haopeng Zhang (grad)
• Thanh Tran (grad)
• Junghee Jo (grad)
• Yanming Nie (visiting)
• Zhao Cao (visiting)

Collaborators

• Charles Sutton (UC Berkeley)

Alumni

• Jagrati Agrawal
• Daniel Gyllstrom
• Richard Cocci
• Hee-Jin Chae
• Eugene Wu (MIT)

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Sponsors

	National Science Foundation
	CAREER: Efficient, Robust RFID Stream Processing for Tracking and Monitoring. Yanlei Diao (PI). National Science Foundation IIS-0746939. Award abstract. This grant supports our research on both the low-level inference and compression over RFID streams and the high-level complex event processing. Any opinions, findings, and conclusions or recommendations expressed at this web site are those of the author(s) and do not necessarily reflect the views of the National Science Foundation.
	Cisco Systems