This document discusses how to capture, analyze, and react to IoT sensor data in real-time. It notes that the amount of IoT data will grow exponentially in coming years, but most data is never analyzed or used. It also explains that the value of most IoT data decays rapidly. The document then provides examples of new low-cost IoT sensors and discusses MQTT as a lightweight protocol for transmitting sensor data. It outlines how to use Apache Spark and machine/deep learning on historical and streaming data to build models. Finally, it discusses challenges like the computational complexity of neural networks and envisions applications of connected vehicles.

1. How to capture, analyse and react on IoT generated
sensor data in real time
Romeo Kienzler, Chief Data Scientist, IBM Watson IoT, WW

2. Why IoT (now) ?
• 15 Billion connected devices in 2015
• 40 Billion connected devices in 2020
• World population 7.4 Billion in 2016

3. Why IoT (now) ?
• 2016 90% of all data generated WW is at the
edge of an IoT device
• This data is never
• captured
• analysed
• acted on

4. Why IoT (now) ?
• 60% of data looses it’s value within milliseconds
of being generated
• New generation of Sensors
• low cost
• low energy consumption
• low data transmission cost
• long life batteries / self supplementary

5. • Energy consumption 0.33333333 µA
• Cost 5 US$
• 600 mA/h
• 70 days
• 1 measurement /h
• Cost 2 US$
• Energy consumption
• Standby 3µA
• Rx 30 mA
• Tx 53 mA
• Range 800m
• Cost 50 US$

6. Why IoT (now) ?
• If a tree falls in the forest we will hear it
• IBM announced to invest 3 billion US$
• Opened IBM Watson IoT Global HQ in Munich, Germany
• As of 2015
• 4000 IoT clients
170 countries
1400 partners
750 IoT patents
1000 Emloyees in HQ

7. IBM and Siemens
• IBM partners with Siemens Buildings
Technologies Division to maximise the
potential of connected buildings
• by the data they create (private side note)

8. IBM and KONE
• IBM partners with KONE on Cloud-based
Embedded intelligence in elevators and
escalators

9. IBM and KONE
• IBM partners with KONE on Cloud-
based Embedded intelligence in
elevators and escalators

10. How 2 IoT?

11. How 2 IoT?
What is MQTT?
•“light weight” telemetry protocol
•Publish-Subscribe protocol via Message Broker
•Invented by IBM 1999
•OASIS Standard since 2013

12. How 2 IoT?

13. How 2 IoT?

19. ApacheSpark
the state-of-the-art in cloud based analytics
Storage Layer (OpenStack SWIFT / Hadoop HDFS / IBM GPFS)
Execution Layer (Spark Executor, YARN, Platform Symphony)
Hardware Layer (Bare Metal High Performance Cluster)
GraphXStreaming SQL MLLib BlinkDB R MLBase
Y
O
U
Intel Xeon E7-4850 v2 48 core, 3 TB RAM, 72 GB HDD, 10Gbps
S
T
R
E
A
M
S

20. Machine Learning on
historic data
Source: deeplearning4j.org

21. Online Learning
Source: deeplearning4j.org

22. online vs. historic
• Pros
• low storage costs
• real-time model update
• Cons
• algorithm support
• software support
• no algorithmic improvement
• compute power to be inline
with data rate
• Pros
• all algorithms
• abundance of software
• model re-scoring / re-
parameterisation (algorithmic
improvement)
• batch processing
• Cons
• high storage costs
• batch model update

23. DeepLearning
DeepLearning
Apache Spark
Hadoop

24. Neural Networks

25. Neural Networks

26. Deeper (more) Layers

27. Convolutional

28. Convolutional
+ =

29. Convolutional

31. Learning of a function
A neural network can basically learn any
mathematical function

32. Recurrent

33. LSTM

34. http://karpathy.github.io/2015/05/21/rnn-effectiveness/

38. •Outperformed traditional methods, such as
•cumulative sum (CUSUM)
•exponentially weighted moving average (EWMA)
•Hidden Markov Models (HMM)
•Learned what “Normal” is
•Raised error if time series pattern haven't been seen
before

39. Learning of a program
A LSTM network is touring complete

40. Problems
• Neural Networks are computationally very complex
•especially during training
•but also during scoring
CPU (2009) GPU (2016) IBM SyNAPSE (2018)

41. DeepLearning
the future in cloud based analytics
Storage Layer (OpenStack SWIFT / Hadoop HDFS / IBM GPFS)
Execution Layer (Spark Executor, YARN, Platform Symphony)
Hardware Layer (Bare Metal High Performance Cluster)
GraphXStreaming SQL MLLib BlinkDB
DeepLearning4J
ND4J
R MLBase H2O
Y
O
U
GPUAVX
Intel Xeon E7-4850 v2 48 core, 3 TB RAM, 72 GB HDD, 10Gbps, NVIDIA TESLA M60 GPU
(cu)BLAS
jcuBLAS
S
T
R
E
A
M
S

42. Why IoT (now) ?
Formal Definition (Romeo Kienzler, 2016)
Cognitive IoT maximises efficiency of the system
under observation by measuring all relevant
parameters in order to (re)act accordingly to
push the system into a state near to the global
optimum

43. My Vision
What if the majority of cars where connected
and sensed? What if we can detect a state of
unpreventable accidents? What if in such a case
we just issue a 30% brake command to all
vehicles? Still a dream?…

44. Do it yourself…
• DeepLearning Architecture on-click cloud
deployment
• to be published:
http://www.ibm.com/developerworks/analytics/
• to be announced:
Twitter: @romeokienzler
• Find this talk on youtube:
http://ibm.biz/romeokienzler