IoT devices are under-utilized - they have amazing sensors and powerful processors, but we use them to just send single temperature values. This is all because we are limited on power and bandwidth, the only way to reach 2, 3 or 10 years of battery life is sending as little as possible. Thus, to make smarter devices we need to process sensor signals directly on the IoT device and just send the conclusions over. Slides from my talk on TinyML and Edge Impulse at The Things Conference 2020 in Amsterdam.

1. Adding intelligence to your
LoRaWAN devices
Jan Jongboom Edge Impulse

2. Jan Jongboom
CTO and co-founder, Edge Impulse
jan@edgeimpulse.com

3. 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

4. But... what does it actually do
Once an hour:
• Average motion (RMS)
• Peak motion
• Current temperature

5. 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

6. Physical world holds a lot of data
>
hZps://rakuten.today/tech-innova[on/simon-crosby-smart-ci[es-edge-compu[ng.html

7. On-device intelligence is the only solution

8. On-device intelligence is the only solution
🚫

9. On-device intelligence is the only solution
Vibra[on paZern
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. Let's fix
this!

12. Machine learning
Classification
What's happening right now?
Anomaly detection
Is this behavior out of the ordinary?
Forecasting
What will happen in the future?

13. Learning tic-tac-toe
X
X
O
O
Content of lucifer box
Every box
resembles a state
of the board

14. Learning tic-tac-toe
X
X
O
O
Content of lucifer box
Every box
resembles a state
of the board

15. Learning tic-tac-toe
X
X
O
O
Content of lucifer box

16. Learning tic-tac-toe
X
X
O
O
Content of lucifer box

17. Learning tic-tac-toe
X
X
X
O
O
Content of lucifer box

18. Learning tic-tac-toe
X
X
X
O
O
Content of lucifer box
Rules
Lose: Remove marble
Draw: Place 1 marble back
Win: Place 3 marbles back

19. X
X
X
O
O
O
Learning tic-tac-toe
Content of lucifer box

20. X
X
X
O
O
O
Learning tic-tac-toe
Content of lucifer box
You lose

21. Learning tic-tac-toe
X
X
O
O
Content of lucifer box

22. Learning tic-tac-toe
X
X
O
O
Content of lucifer box

23. Learning tic-tac-toe
X
X
X
O
O
Content of lucifer box

24. Learning tic-tac-toe
X
X
X
O
O
Content of lucifer box
You win

25. Learning tic-tac-toe
X
X
O
O
Content of lucifer box

26. Training vs. classification
Hundreds of different states
Need to encounter states many times
Training takes long!
Classification is however simple
Play the game, and you have to open up max. 4 drawers!

27. Enabling new use-cases
Sensor fusion
http://www.gierad.com/projects/supersensor/

28. Enabling new use-cases
Anomaly detection

29. Enabling new use-cases
Livestock monitoring
https://os.mbed.com/blog/entry/streaming-data-cows-dsa2017/

30. Practical
usecase

31. Sheep activity tracker
hZps://pixabay.com/photos/sheep-curious-look-farm-animal-1822137/

32. Capturing raw data
11 minutes of raw data over 4 classes. Collected on device, synced over WiFi

33. Extracting features

34. Training two models
Neural network classifier Anomaly detec<on

35. From model to device
Signal processing, neural network and
anomaly detec[on

36. Conclusions back to TTN
♻
Sample for four seconds
Classify
Result differs? Message.
Sheep is walking
hZps://pixabay.com/photos/sheep-curious-look-farm-animal-1822137/

37. Device
80MHz Cortex-M4F processor
128 KiB RAM
SX1276 LoRa radio
Time to analyze 1 second of ac[vity data
(classifica[on + anomaly):

38. Device
80MHz Cortex-M4F processor
128 KiB RAM
SX1276 LoRa radio
Time to analyze 1 second of ac[vity data
(classifica[on + anomaly):
0.008 seconds

39. Bring out the sheep!
Demo <me

40. Recap
The ML hype is real
ML + LoRaWAN = perfect fit
Start using the remaining 99% of sensor data
Sign up now at: edgeimpulse.com

41. Thank you.