Soon WiFi Will Be Able To Tell Not Just Where You Are, But If You're Breathing
New experiments demonstrate indoor WiFi positioning down to a single centimeter.
WiFi networks soon may be able to tell not just where you are, but whether you're even breathing. This is according to research published this month in Computer by a trio of researchers at Peking University that describes a sensing method based not on the conventional received signal strength (RSS) model—which provides information about an environment based on how that environment attenuates a signal—but on an alternative known as channel-state information (CSI), which provides a much richer picture of electromagnetic waves as they bounce around an indoor space.
RSS positioning has been around for about 15 years and was developed into a project called RADAR by researchers at Microsoft. Its operation is pretty crude. Take an indoor space, and then map it out according to WiFi signal strength at different locations. Stick all of this data into a table, and when it comes time to locate an actual device in that space, it's just a matter of matching the observed signal strength to the corresponding location in the table. It's cheap, at least.
Using channel-state information for indoor sensing is already being actively explored, but what the Peking researchers wanted to know is exactly what kind of precision it's capable of offering. To find this out, they applied what's known as the Fresnel zone model, which is easiest to just visualize:
Here's how the paper explains it: "Fresnel zones refer to the series of concentric ellipsoids of alternating strength that are caused by a light or radio wave following multiple paths as it propagates in free space, resulting in constructive and destructive interference as the different-length paths go in and out of phase." You can break any area of space into an infinite number of Fresnel zones.
Dan Wu and his colleagues at Peking University explain that any object that a radio wave encounters as it bounces around a space essentially splits it into two. One is reflected while the other travels on through the object (following a line-of-sight path). At the receiving end of the signal, the two paths recombine, leaving a superimposed signal. It's from the phase difference between the two signals that an intervening object can be inferred. Objects positioned in different Fresnel zones will reflect signals differently, resulting in interference patterns corresponding to different positions.
"We conducted indoor experiments with a pair of Wi-Fi transceivers and a metal cup to verify the Fresnel zones' existence and to show that the received signal varies as expected when an object moves across the zones," Wu and co. write. A set radio frequency was chosen and the metal cup was moved at centimeter intervals in three directions within the Fresnel zone spanning the transceivers. The different positions resulted in the signals superimposed as expected.
So, we wind up with a sensing limit of (at least) a single centimeter. That should be precise enough to detect human respiration, but Wu and his team had to test it out. They found that if the subject was close enough to the line of sight path travelled by the signal between transceivers—and was thus interacting with the signal at its strongest—they could accurately detect breathing. If the subject was too far away from a transceiver or the line of sight path, not so much.
The researchers are nonetheless optimistic about their proof-of-concept: "In the shorter term, we envision the proposed theory accelerating the nonintrusive human-sensing field, enabling a wide spectrum of new applications in homes, offices, hospitals, warehouses, and more. In the longer term, we believe that synergizing communication and sensing capabilities in computing devices will fuel a revolution in both Internet of Things (IoT) and context-aware computing."
Tracking: It's barely even getting started.