Track which room a phone is in inside an office or home without installing any new hardware beacons.
Build an asset tracking system using cheap ESP8266 or ESP32 microcontrollers to send signal fingerprints to the server.
Create a presence detection system that knows when someone has moved from one area of a building to another.
Set up indoor location awareness for a home automation system using existing WiFi access points as landmarks.
Requires running a data storage server and an ML server, plus a training phase of walking through spaces, full setup docs are on the external documentation site.
FIND3, short for Framework for Internal Navigation and Discovery, is an indoor positioning system that lets you track the location of a phone or laptop inside a building without any special hardware. It works by scanning for wireless signals that are already present, such as WiFi access points and Bluetooth devices, and using the pattern of signal strengths to figure out which room or area a device is in. Think of it as a rough indoor equivalent of GPS, where instead of satellites the system reads the radio environment around you. The way it works is a two-step process. First you walk through the spaces you want to map and record signal fingerprints at each location while labeling them. This teaches the system what the radio landscape looks like in each spot. Then when the system runs in tracking mode, it compares live signal readings against those stored fingerprints and predicts where you are. FIND3 uses ten different machine learning classifiers to make these predictions, which gives it more accuracy than relying on a single method. The system is split into several components. A data storage server and a machine learning server handle the backend processing. A command-line scanning tool, an Android app, and code for small ESP8266 and ESP32 microcontrollers can all send fingerprint data to the server. This means you can set it up with a phone, a laptop, or cheap embedded hardware depending on your needs. This is version 3, a full rewrite of earlier versions. Compared to previous releases it adds support for Bluetooth scanning in addition to WiFi, built-in passive scanning, a smaller database format, and a more permissive license. Data is stored in an SQLite database. The project is released under the MIT license. The README is brief and points to a separate documentation site for setup instructions and usage details.
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