In this talk, I explain how to architect your IoT system with Machine Learning.

1. Architecting IoT systems with
Machine Learning
Rudradeb Mitra | https://www.linkedin.com/in/mitrar/
Data Science Summer Conference

2. The vision of IoT by Intel https://www.youtube.com/watch?v=rnDey89wp_M&t=31s

3. How many of you live in this home?

5. What do I want talk about?
• How to build consumer adopted IoT products?
• Through stories of my experiences
• Some problems you can solve!

6. My 15 years on and off affair with IoT
connecting the dots...

7. 2003, Bremen, Germany
RoboCup Japan
2003, Bremen, Germany

8. To solar adoption through satellite images

9. The story of IoT from 2003-2018
IoT

10. So what went wrong...

11. Consumer IoT systems
Database
View data

12. What are the problems?
• Viewing past data has almost no value
• Most IoT startups ended up just collecting big data
• Very few killer applications which creates x5-x10 value

13. Hidden cost of IoT
Relational Data Time series 'Big' Data
(activity, time)(name, address)

14. Relational legacy
data
Big data (eg time-series data)
Database
eg. Postgres
Database
eg. Mongo,
Casandra
Application logic (to split and combine data into
relational / non-relational part)
User Query
SQL query Time series
data query
Database architecture of IoT
@copyright: Rudradeb Mitra

15. Data Volume and Speed
@copyright: Rudradeb Mitra

16. For adoption technology needs to cross
the chasm
• You have to solve a real problem for them.
Build painkillers, not vitamins!

17. So how to cross the chasm?

18. Let me tell you a story...story from one of my startup

19. Driver’s app
Record a trip Trip feedback
Goals &
challenges
Rewards

20. Results
Over 4Mkm driving data

21. IoT Architecture
Database
Engagement
Add a switch to turn off/onView Trip
• Incentivizing
• Nudging
• Not forced

22. What is missing?
Do you think majority will adopt?

24. When there is nothing else, Machine Learning comes to rescue!

25. What is Machine Learning?
• "learn patterns from data without explicitly being programmed"

26. Why is Machine Learning useful?

28. IoT Architecture with Machine Learning
Database
Gamification
Social Engagement
Add a switch to turn off/onView Trip
Predictive +
Analytics
Machine Learning
Algo
@copyright: Rudradeb Mitra

29. What are the values?
• Find risky drivers and adjust their premiums so that majoity pay less
• Predict short and long term future to make roads safer by changing
behaviors

30. Lets see using Machine Learning (esp. Neural Nets)

31. 1. Can we find patterns of risky drivers?

32. Patterns of risky drivers - Clustering
Picture taken from: http://www.ai-junkie.com/ann/som/som1.html
Find patterns in data

33. Self Organizing Maps (Clustering)
0.1
0.3
- Euclidean Distance between
training vector and weights
- The best match node is selected
- Adjust weights of neighboring nodes
to match this weight
0.55
0.15
0.31
0.49
Training
Data
0.6
0.9
0.8
@copyright: Rudradeb Mitra

34. Picture taken from http://www.ai-junkie.com/ann/som/som1.html
Most risky drivers
Most safe drivers
For the driving example

35. And how is that helpful?
• Premiums can be adjusted according to driving behavior and thus
majority will end up paying less

36. 2. Can we go further and predict short term future?

37. word2vec- Predicting next word
word2vec
- Does not understand words or
grammar
How
Feeling
You
Are
Today
Input words
embeddings
Predicted next word
Hidden
Layer
Output word
embedding

38. Word2vec Word embeddings
X.WI.WO

39. Example
word xi
N= 2
0
0
0
1
0 0.3 0 0.7 0 ... 0
0.1
0.77
0.39
.
.
0 0.29 0 0.55 0 ...0.3
0.5
0
Wi =
Wo =
Word embedding for Xi = X. Wi . Wo
Word embedding Xi = [0.33 ... 0.64 ]
0
0
0
1
X =

40. word2vec - 2d space
- Output matrix to 2
dimension using Principal
component Analysis

41. Picture taken from https://www.lucypark.kr/courses/2015-ba/text-mining.html
Germany - France + Paris
= Berlin

42. Prediction for drivers
score acceleration on Friday
score braking on Friday
word2vec
- Using the semantic association
between orders
(Product ID)
(Predicted score on
Sunday)
score braking on Saturday
score speed on Saturday

43. And how is that helpful?
• Advance short term warnings making roads safer and saving lifes

44. 3. And what about long term future?

45. RNN
Y0
X0
RNN
Y1
X1
RNN
Y2
X2
RNN
Y3
X3
RNN
Y4
X4

46. Long Short Term Memory
Cell state
Picture taken from http://colah.github.io/posts/2015-08-Understanding-LSTMs/

47. NN NN NN
f
NN
Forget
gate
Input
gate
Output
gate
New information
Forget an old
information
Updated
information
added
@copyright: Rudradeb Mitra

48. LSTM
cell
LSTM
cell
LSTM
cell
Score Score Score
Driving history
Time 1 Time 2 Time 3
@copyright: Rudradeb Mitra

49. And how is that helpful?
• Provide nudging techniques to change behaviors

50. To summarize
• Roads safer
• Adjust premiums saving money
• Incentive to good behaviors

51. Who will benefit
• Majority of people like you and me, who are GOOD drivers!

52. What other applications are out there?

53. 03/06/2010
2010, Prague, Czech Republic

54. What can we achieve?
• Machine Learning can predict your future consumption (short term,
long term).
• If I know how much I will consume, I can save money and save
electricity!

55. Who will benefit
to people who have been left behind...

56. Decentralized solar energy

57. Output
Fully
connected
pooled
feature maps
pooled
feature maps
feature
maps
feature
maps
Input
image
32x32
Convolutions
6@28x28
Subsampling Convolutions Subsampling
6@14x14 16@10x10 16@5x5
Convolutional Neural Networks

58. Who will benefit
to people who have been left behind...

59. Healthcare
Database
Gamification
Add a switch to turn off
Not to punish, social encouragement
Learning Algorithm
@copyright: Rudradeb Mitra

60. What can we achieve?
• If I know advance health warning, I can save lives!
• Medical cost can do down

61. Retail

62. Future

63. @copyright: Rudradeb Mitra

64. IoT needs Machine Learning
for adoption
Feel free to contact:
https://www.linkedin.com/in/mitrar/
mitra.rudradeb@gmail.com