1. SPIRE: Efficient Data Interpretation and Compression over RFID Streams. Yanming Nie, Richard Cocci, Cao Zhao, Yanlei Diao, and Prashant Shenoy.  IEEE Transactions on Knowledge and Data Engineering (TKDE), 24(1): 141-155, 2012. (pdf)
    Abstract: Despite its promise, RFID technology presents numerous challenges, including incomplete data, lack of location and containment information, and very high volumes. In this work, we present a novel data inference and compression substrate over RFID streams to address these challenges. Our substrate employs a time-varying graph model to efficiently capture possible object locations and inter-object relationships such as containment from raw RFID streams. It then employs a probabilistic algorithm to estimate the most likely location and containment for each object. By performing such online inference, it enables online compression that recognizes and removes redundant information from the output stream of this substrate. We have implemented a prototype of our inference and compression substrate and evaluated it using both real traces from a laboratory warehouse setup and synthetic traces emulating enterprise supply chains. Results of a detailed performance study show that our data inference techniques provide high accuracy while retaining efficiency over RFID data streams, and our compression algorithm yields significant reduction in output data volume.
  2. Distributed Inference and Query Processing for RFID Tracking and Monitoring. Zhao Cao, Charles Sutton, Yanlei Diao, and Prashant Shenoy.  Journal "Proceedings of the VLDB Endowment" (PVLDB), Volume 4(5), pages 326-337, February 2011. (pdf)
    Abstract: In this paper, we present the design of a scalable, distributed stream processing system for RFID tracking and monitoring. Since RFID data lacks containment and location information that is key to query processing, we propose to combine location and containment inference with stream query processing in a single architecture, with inference as an enabling mechanism for high-level query processing. We further consider challenges in instantiating such a system in large distributed settings and design techniques for distributed inference and query processing. Our experimental results, using both real-world data and large synthetic traces, demonstrate the accuracy, efficiency, and scalability of our proposed techniques.
  3. Architectural Considerations for Distributed RFID Tracking and Monitoring. Zhao Cao, Yanlei Diao, and Prashant Shenoy. NetDB 2009 (pdf)
    Abstract: In this paper we discuss architectural challenges in designing a distributed, scalable system for RFID tracking and monitoring. We argue for the need to combine inference and query processing techniques into a single system and consider several architectural choices for building such a system. Key research challenges in designing our system include: (i) the design of inference techniques that span multiple sites, (ii) distributed maintenance of inference and query state, (iii) sharing of inference and query state for scalability, and (iv) the use of writeable RFID tags to transfer state information as objects move through the supply chain. We also present the status of our ongoing research and preliminary results from an early implementation.
  4. Probabilistic Inference over RFID Streams in Mobile Environments. Thanh Tran, Charles Sutton, Richard Cocci, Yanming Nie, Yanlei Diao, and Prashant Shenoy. ICDE 2009. (pdf) (tech report version)
  5. Efficient Data Interpretation and Compression over RFID Streams. Richard Cocci, Thanh Tran, Yanlei Diao, and Prashant Shenoy. ICDE 2008. (pdf) (tech report)
  6. SPIRE: Scalable Processing of RFID Event Streams. Richard Cocci, Yanlei Diao, and Prashant Shenoy. In Proceedings of the 5th RFID Academic Convocation, April 2007. (pdf)