Radio frequency identification (RFID) technology embeds small, inexpensive, and passively-powered radio tags in physical objects. Many potential innovative applications of RFID are not yet supported by current technology. This research pursues a multidisciplinary, integrative approach to investigate fundamental technical challenges in RFID, both at the physical and data management layers, to realize innovative data-centric applications such as attribute-based object search. The research focuses on two areas, distributed data management, and (ii) physical layer limitations. The research will investigate data structures that can resolve queries for remote objects by only accessing local tags within a specified radio range; efficient encoding of symbolic and numeric logical attributes to address the cost of associative search and random access to a large data structure from a single tag; and redundancy-based algorithms for overcoming data access reliability limitations and access failures in dense tag environments. At the physical layer, the research will address issues due to tag density, the need for extended memory storage, and the absence of on-chip power.

This research has the potential to aid individuals in living in increasingly complex environments. The research will aid in the development of new course modules on large-scale dense RFID simulation at the University of Washington and robust information dissemination at Arizona State University. The project will also allow students at both universities to experiment with various aspects of RFID-based distributed information management, thus increasing student awareness of mechanisms for addressing information-overload and privacy issues that arise due to increasingly widespread, yet loosely controlled, digital "footprints."