Wireless network modded to see through walls

Maybe it’s time to go back to a wired connection.

 
The way signal strength varies in a wireless network can reveal what’s going on behind closed doors

It’s every schoolboy’s dream: an easy way of looking through walls to spy on neighbours, monitor siblings and keep tabs on the sweet jar. And now a dream no longer…

Researchers at the University of Utah say that the way radio signals vary in a wireless network can reveal the movement of people behind closed doors. Joey Wilson and Neal Patwari have developed a technique called variance-based radio tomographic imaging which processes the signals to reveal signs of movement. They’ve even tested the idea with a 34-node wireless network using the IEEE 802.15.4 wireless protocol. This is the protocol for personal area networks employed by home automation services such as ZigBee.

The basic idea is straightforward. The signal strength at any point in a network is the sum of all the paths the radio waves can take to get to the receiver. Any change in the volume of space through which the signals pass, for example caused by the movement of a person, makes the signal strength vary. So by “interrogating” this volume of space with many signals, picked up by multiple receivers, it is possible to build up a picture of the movement within it.

In tests with a 34-node network set up outside a standard living room, Wilson and Patwari say they were able to locate moving objects in the room to within a metre or so. That’s not bad and the team say there is ample potential for improvement by increasing accuracy while reducing the number of nodes.

The advantage of this technique over others is first its cost. The nodes in such a network are off the shelf and therefore cheap. Other through-wall viewing systems cost in excess of $100,000. The second advantage is the ease with which it can be set up. Wilson and Patwari say that adding a GPS receiver to each node allows it work out its own location which should dramatically speed up the imaging process.Other systmes have to be “trained” to recognise the environment.

Wilson and Patwari have even worked out how their system might be used:

“We envision a building imaging scenario similar to the following. Emergency responders, military forces, or police arrive at a scene where entry into a building is potentially dangerous. They deploy radio sensors around (and potentially on top of) the building area, either by throwing or launching them, or dropping them while moving around the building. The nodes immediately form a network and self-localize, perhaps using information about the size and shape of the building from a database (eg Google maps) and some known-location coordinates (eg using GPS). Then, nodes begin to transmit, making signal strength measurements on links which cross the building or area of interest. The received signal strength measurements of each link are transmitted back to a base station and used to estimate the positions of moving people and objects within the building.”

That’s ambitious but shoudl they ge ttheir system to the point where it can be used like this it raises another problem: the issue of privacy.

The broader issue is how such a cheap and easy-to-configure monitoring networks might be used if they become widely available. What’s to stop next door’s teenage brats monitoring your every move or house breakers choosing their targets on the basis that nobody is inside?

Of course, in the cat and mouse game of surveillance, it shouldn’t be too hard to build a device that disables such a monitoring network. But only if you know it’s there in the first place.

There are fun and games galore to be had with this idea.

Ref: arxiv.org/abs/0909.5417: Through-Wall Motion Tracking Using Variance-Based Radio Tomography Networks

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