Arlington, VA

CNA brought home a first-place win in the Smart Communities, Smart Responders: An Artificial Intelligence for Internet of Things Prize Competition (AI3 Prize Competition). In partnership with the software development company RIIS, CNA analysts built the First Responder Awareness Monitoring during Emergencies (FRAME) system to address the competition challenge.

"Winning the AI3 competition is incredibly exciting and fills our team with motivation to face new challenges," said CNA Research Analyst John Crissman. "We eagerly look forward to enhancing FRAME's capabilities, rigorously testing its functionalities, and preparing it to deliver a game-changing solution for the first responder community."

The competition was led by Texas A&M University and US-Ignite, funded by the Public Safety Communications Research Division of the National Institute of Standards and Technology. The premise of the challenge was that cities and towns are rapidly adopting technology and evolving into "smart cities," with more sensors and devices collecting data that can be used to provide more efficient and effective operations. The AI3 challenge asked innovators to harness machine learning technologies to collect, process, and act on the vast quantity of known and unknown data streams from these smart cities in an emergency situation.

"Every day our CNA family works to provide solutions to national safety and security issues," said CNA President and CEO Dr. Katherine McGrady. "This win is another example of innovative minds working together to anticipate challenges, collaborate, and find solutions to make our communities safer. Congratulations to the team."

The competitors were given a scenario in which an earthquake hits a city, bringing significant devastation. The severity of the disaster requires mutual aid from several nearby cities, and the need to aggregate and share information across different systems to support an organized, effective response. The CNA-RIIS team's FRAME concept was designed to address these challenge components by taking in vast amount of data from smart city sensors, using machine learning to interpret the information, and aggregating that into a common data view to provide increased situational awareness in the emergency.

Earlier this month, the CNA-RIIS team pitted their FRAME system against the other competition finalists at the Disaster City location within the Texas A&M Engineering Extension Service.. The CNA members of the team are John Crissman, Shaelynn Hales, Addam Jordan, Mark Lesko, Matt Prebble, Dawn Thomas, Simone Walker, and Rebekah Yang.