How to capture, analyse and react on IoT generated sensor data in real time

Romeo Kienzler, Chief Data Scientist, IBM Watson IoT, WW

Why IoT (now) ?• 15 Billion connected devices in 2015

• 40 Billion connected devices in 2020

• World population 7.4 Billion in 2016

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

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

• 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$

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

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)

IBM and KONE• IBM partners with KONE on Cloud-based

Embedded intelligence in elevators and escalators

IBM and KONE• IBM partners with KONE on Cloud-

based Embedded intelligence in elevators and escalators

How 2 IoT?

How 2 IoT?

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

How 2 IoT?

How 2 IoT?

ApacheSparkthe 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 MLBaseY O U

Intel Xeon E7-4850 v2 48 core, 3 TB RAM, 72 GB HDD, 10Gbps

S T R E A M S

Machine Learning on historic data

Source: deeplearning4j.org

Online Learning

Source: deeplearning4j.org

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

DeepLearningDeepLearning

Apache Spark

Hadoop

Neural Networks

Neural Networks

Deeper (more) Layers

Convolutional

Convolutional

+ =

Convolutional

Learning of a function

A neural network can basically learn any mathematical function

Recurrent

LSTM

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

• 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

Learning of a program

A LSTM network is touring complete

Problems• Neural Networks are computationally very complex• especially during training• but also during scoring

CPU (2009) GPU (2016) IBM SyNAPSE (2018)

DeepLearningthe 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 BlinkDBDeepLearning4J ND4J

R MLBase H2OY 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

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

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?…

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