Our (used to) lighting division is selling a product where we use our LED-lights to track phones indoor. Always thought it one of the coolest applications of our tech. Using the fact that we can control LED frequencies invisible to the human eye but visible to a phones camera to pinpoint devices and provide indoor navigation [0]. Accuracy was tested to be 30cm (when combined with a few other factors).<p>[0] <a href="http://www.lighting.philips.com/main/systems/lighting-systems/indoor-positioning#" rel="nofollow">http://www.lighting.philips.com/main/systems/lighting-system...</a>
I have created a find3 beacon/client for the cheap ESP8266/ESP32 microcontrollers yesterday and released it to the public.
<a href="https://github.com/DatanoiseTV/esp-find3-client" rel="nofollow">https://github.com/DatanoiseTV/esp-find3-client</a><p>The ESP8266 uses WiFi and submits the BSSIDs and their RSSI to the server.<p>The ESP32 version uses BLE probes additionally, which potentially improves accuracy.<p>Any feedback is welcome. This is an early hack.
The byline says "High-precision" but I could not find any benchmarks or scientific analysis. What does high precision mean here? How stable is it?<p>What about privacy of the users? Does the server know where everyone is?
Just be aware that the public server in the app has no access control. Your location will be published.<p>You probably really want to set up a private server to use this in practice or even try it out.<p>Otherwise looks promising. A nice addition to my home setup
Hey, I remember this project! I had the chance to give a bit of advice to the creator, Zach Scholl while I was working at Duke's Co-Lab [1]. It was cool then, and looks like it's only improved in robustness since (and it sounds like HN has plenty of ideas for PRs).<p>Although I no longer work/learn at the Co-Lab I'd love to plug the value of these sorts of programs at schools. They give encouragement with money and publicity, and most importantly, tools (VMs/APIs) and advice ("why won't this compile?"/"where do we start?"). I had a chance to see and help with a lot of projects that wouldn't have otherwise been possible to get built. Hopefully it helped a few people get into hacking that wouldn't have otherwise too!<p>[1]: colab.duke.edu
Haven't read through the code yet, but the only thing I need to know is if these guys know what Rayleigh scattering is. If not, this probably isn't going to work so great.<p>I worked on an indoor tracking system several years ago that used custom hardware. Since then, I've seen a lot of crappy solutions that use BLE RSSI measurements. This is simply not an easy problem to solve with crude methods.
Pardon the laymen’s questions, but my understanding of precision location is that it requires triangulation + accurate timestamps (the more accurate the more precise), plus with indoor the additional complexity of resolving signal multi path.
There’s no mention of the hardware or “system” requirements. Possibly all modern smartphones and laptops have the necessary hardware? It would still be nice for this to mention a high level summary of the system required to set this up. How many devices to coordinate the location discovery?
Really cool idea, and it works really well. Was able to do very precise positioning in my house, even within a single room if given multiple references.
Ultimately it drained the phone battery too much to be of actual use for me in my smarthome.
Every URL I am trying on <a href="https://cloud.internalpositioning.com" rel="nofollow">https://cloud.internalpositioning.com</a> is reporting 502 bad gateway. Is the hosted service currently down?
I wonder if the NSA has access to this kind of data at all. Theoretically if you could put a backdoor in iOS or Android you could create a nation-wide Wifi map to track people/devices with.
pretty neat.<p>how robust are the fingerprints across devices i wonder and whether this has been experimented in with strong multipath effects (thereby confounding fingerprinting).<p>edit:<p>another interesting thing is that this is basically context sensitive hashing of tuples being done using ml classification techinques