Queralt Developing Behavior-Monitoring RFID Software
The Connecticut startup has received a $100,000 grant from the DHS to work on an intelligent system that can detect behavioral patterns based on data gathered from RFID tags and sensors.
Apr 23, 2009—Using funds it received from a Small Business Innovation Research (SBIR) grant through the U.S. Department of Homeland Security (DHS), auto-ID solutions provider and systems integrator Queralt is building an intelligent system that learns from data collected via RFID and sensors.
The development effort builds on the Wallingford, Conn., company's existing RFID technology, as well as an integrated behavioral learning engine that enables the system to, in effect, learn an individual's or asset's habits over time. The DHS grant was awarded based on the system's ability to track and monitor individuals and assets for security purposes.
Since its founding three years ago, the company has commercially deployed a handful of systems, including an RFID-based system at New Haven Public Schools in Connecticut, to help the school district monitor environmental conditions in its electronics storage closets, as well as track the locations of laptop computers within school buildings (see New Haven Public Schools Keeps Tabs on Laptops).
The SBIR grant is for $100,000, and is part of a grant program initiated in 2004 by the DHS. The department provides the grants through the federal government's SBIR program, designed to offer small businesses the opportunity to propose innovative ideas that meet the government's specific research and development needs. The SBIR program, offered through the DHS' Science and Technology (S&T) Directorate and the Domestic Nuclear Detection Office (DNDO), specifically seeks initiatives relevant to the department's various divisions: chemical and biological, borders and maritime security, human factors, explosives, infrastructure and geophysical, and command, control and interoperability.
The core of Queralt's system is the behavioral engine that includes a database, a rules engine and various algorithms. Information acquired by reading a tag on an asset or an individual, as well as those of other objects or individuals with which that asset or person may come into contact, and information from sensors (such as temperature) situated in the area being monitored, are fed into the engine. The engine then logs and processes the data to create baselines, or behavioral patterns. As baselines are created, rules can be programmed into the engine; if a tag read or sensor metric comes in that contradicts the baseline and/or rules, an alert can be issued. Development of the behavioral engine is approximately 85 percent done, Queralt reports, and a prototype should be ready in a few months.
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