Regular old WiFi spots dangerous objects in bags

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It’s possible to use ordinary WiFi to detect weapons, bombs, and explosive chemicals in bags at places like stadiums, theme parks, schools, and other public venues, research shows.

The researchers’ suspicious object detection system is easy to set up, reduces security screening costs, and avoids invading privacy such as when screeners open and inspect bags, backpacks, and luggage. Traditional screening typically requires high staffing levels and costly specialized equipment.

“In large public areas, it’s hard to set up expensive screening infrastructure like what’s in airports.”

“This could have a great impact in protecting the public from dangerous objects,” says Yingying (Jennifer) Chen, study coauthor and a professor in the department of electrical and computer engineering in Rutgers–New Brunswick’s School of Engineering. “There’s a growing need for that now.”

WiFi, or wireless, signals in most public places can penetrate bags to get the dimensions of dangerous metal objects and identify them, including weapons, aluminum cans, laptops, and batteries for bombs. WiFi can also estimate the volume of liquids such as water, acid, alcohol, and other chemicals for explosives, according to the researchers.

This low-cost system requires a WiFi device with two to three antennas and can be integrated into existing WiFi networks. The system analyzes what happens when wireless signals penetrate and bounce off objects and materials.

Experiments with 15 types of objects and six types of bags demonstrated detection accuracy rates of 99 percent for dangerous objects, 98 percent for metal, and 95 percent for liquid. For typical backpacks, the accuracy rate exceeds 95 percent and drops to about 90 percent when objects inside bags are wrapped, Chen says.

“In large public areas, it’s hard to set up expensive screening infrastructure like what’s in airports,” Chen says. “Manpower is always needed to check bags and we wanted to develop a complementary method to try to reduce manpower.”

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Next steps include trying to boost accuracy in identifying objects by imaging their shapes and estimating liquid volumes, she says.

The peer-reviewed study was part of the 2018 IEEE Conference on Communications and Network Security on cybersecurity. The study, which researchers at the Wireless Information Network Laboratory in the School of Engineering led, also included engineers at Indiana University-Purdue University Indianapolis and Binghamton University.

Source: Rutgers University