Whereami – Using WiFi signals and machine learning to predict where you are

  • This Python package is a nice port of a system I wrote [1] which I call FIND (the Framework for Internal Navigation and Discovery) [2]. I believe whereami mostly supports laptops, but with FIND you can do internal positioning on a lot devices - Android phones, Electric Imps, Raspberry Pis, Particles, and soon ESP8266s [3] and even iPhones if you are willing to go through some extra setup. [4]

    [1]: https://news.ycombinator.com/item?id=11517667

    [2]: https://github.com/schollz/find

    [3]: https://www.internalpositioning.com/client/

    [4]: https://github.com/schollz/find-lf

  • I did something similar for Android a while ago https://guidefreitas.github.io/programming/osx/docker/2015/0...

  • I have a really dumb version of this without any ML at work, it uses the API on our Ubiquiti Unifi's controller to get a list of devices connected to each of our 4 AP's.

    Its good enough to see if somebody is in a meeting room and unavailable or if they are probably free.

    If you have more than one device on the network that belongs to one person, it uses the device that has been connected to an AP for the least time (theoretically that's the last one that moved)

  • I wonder if there's enough signal to detect if someone enters the room, which could be another cool trigger (e.g., turn on lights).

  • Is there any reason this couldn't be ported to Android or iOS, after hacking together an interface using something like Toga?

  • I wonder if it would be possible to create a coordinate system from this. This is, without any associations, map where each router is based on closeness to other routers and do a sort of "triangulation" to get coordinates.

  • Can I do this with a single wifi router in my house or is it saying I need 7 "routers" or access points for this to work?

  • It's awesome seeing open source implementations of this. We were doing this in an R&D lab of a major tech company in 2009.