A new ultralight security system operating off of Wi-Fi signals could help protect schools and other public places that terrorists might target. Led by a team of researchers from the Wireless Information Network Laboratory (WINLAB) at Rutgers University, the low-cost system uses just two or three antennas that can be integrated into existing wireless systems.
“Our system can be easily deployed to many places that still have no pre-installed security check infrastructures (e.g., airport) and require high-manpower to conduct security checks such as theme parks, museums, stadiums, metro/train stations and scenic locations,” wrote the authors.
For typical backpacks, the system is able to accurately detect suspicious objects 95 percent of the time and is able to identify 90 percent of the dangerous material types in the study. It could be increasingly beneficial in light of recent events, such as the 2013 Boston Marathon bombing that left three dead and injured another 264. Just last year, a gunman opened fire on a concert crowd in Las Vegas, killing 58 and injuring 564 others. In both instances, the attackers were able to conceal their weapons in bags without drawing attention to themselves in public places. To combat attacks like this, school officials in Florida now only allow students to carry clear and transparent backpacks, following a shooting earlier this year that killed 17 people. As the authors note, such measures can infringe on personal privacy.
The new system incorporates fine-grained channel state information (CSI) to detect suspicious objects and identify the type of material they are made of. Fibers in baggage allow the wireless signals to pass through, and the system then records how signals interact with what is inside the bag. Most dangerous objects are made from metal or liquid, so the system determines an object’s level of risk by examining how much water or metal it has as these properties change wireless signals by absorbing, refracting, or reflecting them.
Researchers tested 15 different types of objects hidden in six types of bags. The system accurately identified dangerous objects 99 percent of the time, with metal at rates of 98 percent and liquids at 95 percent. The team says they will next try to increase accuracy by imaging the shape and liquid volumes identified in objects.