Join me implementing a neural network to improve accuracy of an OpenSource indoor location tracking system

To all techies reading this:

GIST: I am looking for interested hackers who want to help me implement a neural network that improves the accuracy of bluetooth low energy based indoor location tracking.

Longer version:

I am currently applying the last finishing touched to a house wide bluetooth low energy based location tracking system. (All of which will be opensourced)

The system consists of 10+ ESP-32 Arduino compatible WiFi/Bluetooth system-on-a-chip. At least one per room of a house.

These modules are very low powered and have one task: They scan for BLE advertisements and send the mac and manufacturer data + the RSSI (signal strength) over WiFi into specific MQTT topics.

There is currently a server component that takes this data and calculates a probable location of a seen bluetooth low energy device (like the apple watch I am wearing…). It currently is using a calibration phase to level in on a minimum accuracy. And then simple calculation matrices to identify the most probable location.

This all is very nice but since I got interested in neural networks and KI development – and I think many others might as well – I am asking here for also interested parties to join the effort.

I do have an existing set-up as well as gigabytes of log data.

I know about previous works like „Indoor location tracking system using neural network based on bluetooth

Now I am totally new to the overal concepts and tooling and I start playing with TensorFlow right now.

If you want to join, let me know by commenting!


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