Adding intelligence to your LoRaWAN deployment - The Things Virtual Conference
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LoRaWAN devices are typically simple, they grab some sensor data and deliver it back to the network. By adding some embedded machine learning we can make them a lot more intelligent!
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Adding intelligence to your LoRaWAN deployment - The Things Virtual Conference
- 1. Adding intelligence to your LoRaWAN devices Jan Jongboom Edge Impulse
- 2. Yes, slides are available h-p://janjongboom.com Wri-en tutorial: h-ps://www.edgeimpulse.com/blog/adding- machine-learning-to-your-lorawan-device/
- 3. Jan Jongboom CTO and co-founder, Edge Impulse [email protected]
- 4. Typical LoRaWAN sensor in 2020 Vibration sensor (up to 1,000 times per second) Temperature sensor NFC Water & explosion proof Processor capable of running >20 million instructions per second
- 5. But... what does it actually do Once an hour: • Average motion (RMS) • Peak motion • Current temperature
- 6. 99% of sensor data is discarded due to cost, bandwidth or power constraints https://www.mckinsey.com/~/media/McKinsey/Business%20Functions/McKinsey%20Digital/Our%20Insights/ The%20Internet%20of%20Things%20The%20value%20of%20digitizing%20the%20physical%20world/The-Internet-of-things- Mapping-the-value-beyond-the-hype.ashx
- 7. Lots of interesting events get lost Peak
- 8. Single numbers can be misleading updown circle avg. RMS 3.3650 3.3515
- 9. On-device intelligence is the only solution Vibraon pa-ern heard that lead to fault state in a weekTemperature varies in a way that I've never seen before Machine oscillates different than all other machines in the factory
- 10. On-device intelligence is the only solution Temperature varies in a way that I've never seen before (0x1)
- 11. Machine learning is great at finding pa1erns in messy data (anything you can't reason about in Excel)
- 13. TinyML Inspired by "OK Google" Focus on inferencing, not training Machine learning model is just a mathemacal funcon with lots of parameters Accuracy vs. speed, reducing parameters, hardware-opmized paths Targeng ba-ery-powered microcontrollers Pete Warden Neil Tan
- 14. What is it good for? https://www.flickr.com/photos/oceanyamaha/7091324605 Recognizing sounds Detecting abnormal vibration https://pixabay.com/photos/washing-machine-wash-cat-4120449/ Biosignal analysis https://www.flickr.com/photos/sheishine/16696564563 Anything with messy, high-resolu9on sensor data
- 15. From 0 to model
- 16. 1. Everything starts with raw data Get data at the highest resolu9on possible - e.g. using serial or directly over WiFi
- 17. 2. Extract meaningful features Very dependent on your use case Raw data can be notoriously hard to deal with (3s. accelerometer data = 900 data points, 1s. audio data = 16,000 data points) Raw data is messy -1.1200, 0.5300, 10.6300, -0.5200, 1.1600, 13.1600, -0.1600, 1.4200, 13.5900, -0.2400, 1.3200, 10.8500, -0.3700, 0.5100, 7.7800, -0.1900, 0.0500, 8.8400, -0.1900, 0.0500, 8.8400, 0.2400, 0.7300, 11.1900, 0.5200, 1.6500, 12.5200, 0.6400, 1.5000, 10.8200, 0.2900, 0.3300, 7.4000, -0.3100, -0.5600, 8.5900, -0.8800, 0.6600, 11.9200, -0.8800, 0.6600, 11.9200, -0.4500, 1.3900, 11.9100, -0.0800, 1.6000, 12.7800, -0.1100, 0.3600, 9.1700, -0.0200, 0.0700, 9.8200, 0.0600, 0.9600, 12.0000, 0.2800, 1.7700, 13.2000, 0.2800, 1.7700, 13.2000, 0.2300, 1.5100, 12.6000, 0.2700, 1.0800, 11.4300, 0.0900, 0.9300, 10.9400, 0.1700, 1.2100, 11.0400, 0.4100, 1.8500, 11.5000, 0.3900, 1.9600, 11.4300, 0.3900, 1.9600, 11.4300, 0.2400, 1.4400, 10.7200, 0.0300, 1.1900, 10.3600, -0.0300, 1.3000, 10.6100, 0.4800, 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- 18. Example of a signal processing pipeline 32,000 => 240
- 19. Before and after feature extraction
- 20. 3. Letting the computers figure it out Classification Neural network Anomaly detection K-means clustering Forecasting Regression
- 21. 4. Deploying Signal processing, neural network and anomaly detecon
- 22. Conclusions back to TTN ♻ Sample for four seconds Classify Result differs? Message. Sheep is walking h-ps://pixabay.com/photos/sheep-curious-look-farm-animal-1822137/
- 23. Getting started
- 24. Get some hardware ST B-L475E-IOT01A 80MHz, 128K RAM, $50 Any smartphone Any dev board w/ the ingestion service
- 25. Connectivity back to TTN
- 26. Edge Impulse - TinyML as a service Embedded or edge compute deployment options Test Edge Device Impulse Dataset Acquire valuable training data securely Test impulse with real-time device data flows Enrich data and generate ML process Real sensors in real time Open source SDK Free for developers: edgeimpulse.com
- 27. End-to-end tutorials on vibra9on and audio: docs.edgeimpulse.com
- 28. Let's build a model! h-ps://pixabay.com/photos/sheep-curious-look-farm-animal-1822137/
- 29. Recap The ML hype is real ML + LoRaWAN = perfect fit Start using the remaining 99% of sensor data Sign up now at: edgeimpulse.com
- 30. Thank you.