ANALYTICS IN IOT SPACE

By :-• Mitesh Gupta

AGENDA

What is IoT?

What is IIoT

What is AoT

How IoT Analytics is Different?

Case Studies

Data Science (necessity and importance)

Analytics Way Forward

Technology Stack for Analytics

Questions & Answers

WHAT IS…

BASIC DEFINITION

• Forbes : This term refers to devices that collect & transmit data via the

internet.

• SAP : More than 50 billions objects will connect to internet and this

connection is called as IoT. This “things” talk to each other,

collect streaming data and insights.

• Cisco : The IoT links smart objects to the Internet. It can enable an

exchange of data never available before, and bring users

information in a more secure way.

• Wiki : The Internet of Things (IoT) is the network of physical

objects/devices that enables these objects to collect and

exchange data.

THINGS IN INTERNET OF THINGS

• Objects

• Machines

• Appliances

• Building

• Vehicle

• People and many more…

WHAT IS INTERNET OF THINGS!

6

ApplicationServer

Things

Web

Services

IOT ECOSYSTEM

Remote

Internet Network

Data Storage

Analytics

IoT Devices

Analy

sis

Com

mand

Gateway

M2M

• Machine to machine refers to direct communication between

devices using any communication channel, including wired and

wireless.

• Machine to machine communication can include industrial

instrumentation, enabling a sensor or meter to communicate

the data it records.

M2M VS IOT

IMPORTANCE

• Connect with things

• Monitoring of things

• Search for things

• Manage things

• Control things

GARTNER: EMERGING TECHNOLOGY HYPE CYCLE

MARKET DRIVERS & BARRIERS

Four Market Drivers•Expanded internet connectivity•High mobile adoption•Low-cost sensors

•Large IoT investments

Four Barriers•Security concerns•Privacy concerns•Implementation problems

•Technological fragmentation

SIZE IN MARKET

$6TRILLION

INVESTED$6 trillion will be investedon IoT solutions over the next five years 24 BILLION

There will be 24 billion IoT devicesinstalled by 2020

$13

TRILLION ROITotal investments over the next five years

will generate $13 trillion by 2025

ENTITIES USING IOT ECOSYSTEM

Consumers

5B Devices Installed By

2020

Governments

7.7B Devices Installed By

2020

Businesses

11.2B Devices Installed

By 2020

$3B Spent (2015-2020)

$2.1B Spent (2015-2020)

$900M Spent (2015-2020)

$7.6B ROI (2015-2025)

$1.4B ROI (2015-2025)$4.7B ROI (2015-2025)

VERTICALS UTILIZE IOT ECOSYSTEM

Transportation

Manufacturing

Connected Homes

Agriculture

Oil & Gas, Mining

Utilities

Infrastructure

Health Care

Many more….

INDUSTRIAL INTERNET OF THINGS (IIOT)

• A universe of intelligent industrial products, processes and services that

communicate with each other and with people over a global network -

Accenture

• The Industrial Internet of Things (IIoT) is the next wave of innovation

impacting the way the world connects and optimizes machines. The IIoT,

through the use of sensors, advanced analytics and intelligent decision

making, will profoundly transform the way field assets connect and

communicate with the enterprise – EDN Networks

• The Industrial Internet of Things is an evolution of existing technologies that

enables end users to improve processes, drive productivity, and maintain an

edge in our increasingly competitive global economy - Kepware

IOT & IIOT

Source – Control

Engineering

IIOT FRAMEWORK

Source - EDN Networks

KEY CHALLENGES IN IIOT

• Settling on device capabilities

• Security

• Bridging the gaps that divide us (people)

IIOT & ITS IMPACT

20

Workforce

Advanced

Analytics

Intelligent

Machine

INTRODUCTION OF AOT

Connected Devices

Embedded Analytics Smart Devices

Analytics enables to make predictions and send alerts/notifications from streaming data(real time sensor data) using automated analytics platform

Analytics decode and transform the continuous flow of M2M data into value-added information

WHAT MAKES IOT ANALYTICS DIFFERENT

CASE STUDY

• Preventive Maintenance

• Freezer Failure (Proactive Failure Detection)

• Solar PV Plant (Real Time Analytics)

1. PREVENTIVE MAINTENANCE

1. PREVENTIVE MAINTENANCE (CONT.)

1. PROACTIVE FAILURE DETECTION

1. PROACTIVE FAILURE DETECTION (CONT.)

1. PROACTIVE FAILURE DETECTION (CONT.)

1. PROACTIVE FAILURE DETECTION (CONT.)

1. PROACTIVE FAILURE DETECTION (CONT.)

SOLAR UTILITY EPC COMPANY

31

Increasing Challenges

How do you create a Competitive Advantage ?

Tariff ratesO&M

expenses

Remote

locations

32

LETS TRACK A 20 MW SOLAR PLANT

3

2

4

1

5

6

7

9

8

Remote Monitoring & Control, Diagnostics and Analytics are very CRITICAL for

Solar plants!

QUANTUM OF DATA : 20MW SOLAR PV PLANT

CONFIDENTIAL & PROPRIETARY DOCUMENT

Sr. No. Device Type Total Devices Frequency Daily (B) Monthly (B)

1 Combiner Box 208 1 Hz 61,10,20,800 18,33,06,24,000

2 INVERTER 26 1 Hz 58,81,07,520 17,64,32,25,600

3 TRAFO 13 1 Hz 17,59,680 5,27,90,400

4 MFM 17 1 Hz 1,91,43,360 57,43,00,800

5 VCB 15 1 Hz 2,16,000 64,80,000

6 PLANT 1 1 Hz 12,24,000 3,67,20,000

7 ZONE 8 1 Hz 11,75,040 3,52,51,200

8 WS 1 1 Hz 5,63,040 1,68,91,200

9 FAN 26 1 Hz 37,440 11,23,200

10 UPS 8 1 Hz 45,04,320 13,51,29,600

11 LDB 8 1 Hz 46,080 13,82,400

12 ACDB 8 1 Hz 11,520 3,45,600

13 SURGEARRESTOR 208 1 Hz 2,99,520 89,85,600

14 TEMPSENSOR 6 1 Hz 1,46,880 44,06,400

Total (Bytes) 36,84,76,56,000

Total (GigaBytes) 34.3171

HOW TO DIGEST THIS HUMONGOUS DATA FOR DATA INSIGHTS

CONFIDENTIAL & PROPRIETARY DOCUMENT

• Performance Analytics

• Real Time Benchmarking

• Trend Analysis

• Adaptive Machine Learning Algorithms

Performance Analytics

Trend

AnalysisMachine

Learning

Real Time

Benchmarking

PERFORMANCE ANALYTICS

CONFIDENTIAL & PROPRIETARY DOCUMENT

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REAL TIME BENCHMARKING

CONFIDENTIAL & PROPRIETARY DOCUMENT

TREND ANALYSIS

CONFIDENTIAL & PROPRIETARY DOCUMENT

TREND ANALYSIS

CONFIDENTIAL & PROPRIETARY DOCUMENT

ADAPTIVE MACHINE LEARNING ALGORITHMS

CONFIDENTIAL & PROPRIETARY DOCUMENT

CHALLENGES WITH ANALYTICS IN IOT

• Data is growing bigger

• Data in motion

• Geographically distribution of data

• New functionality and tools (capability perspective)

TOP 10 MARKET PLAYERS IN IOT

We are Engineer’s and we can really connect

better to machines rather than human beings…!!

IoTIt will change our lives

DATA SCIENCE

WHY THE WORLD IS LOOKING AT

IT?

SOME STATS ON DATA SCIENCE:

• 25% of organizations now have a data scientist on staff.

• By increasing the usability of data by just 10%, the average Fortune 100 company could expect an increase of $2 billion dollars (source: Fathom)

• 1,40,000 to 1,90,000 people with deep analytic skills as well as 1.5 million managers and analysts will be needed by 2018 to fill jobs in Big Data in US (source: McKinsey)

• 86% of people are willing to pay more for a great customer experience with a brand (source: Lunch Pail)

• By 2020 the IoT Will Include 26 Billion Units, Creating New Challenges for All Aspects of the Data Center (source : Gartner)

UNDERSTANDING DATA SCIENCE

WHAT IS ANALYTICS?

Maturity of Analytics Capabilities

Co

mp

etit

ive

Ad

van

tage

Raw Data

Cleaned Data

Standard Reports

Ad Hoc Reports & OLAP

Predictive Modelling

Optimisation

What Happened? – Descriptive Analytics

Why did it happen? – Diagnostic Analytics

What will happen? – Predictive Analytics

What is the best that could happen? Prescriptive Analytics

Sense & Respond Predict & Act

Generic Predictive Analytics

Source: SAP

MACHINE LEARNING

• Machine learning is a subfield of computer science that evolved from

the study of pattern recognition and computational learning theory in

artificial intelligence.

- Wikipedia

• Machine learning is a type of artificial intelligence (AI) that provides

computers with the ability to learn without being explicitly

programmed. Machine learning focuses on the development of

computer programs that can teach themselves to grow and change

when exposed to new data.

- whatis.techtarget.com

TYPE OF PROBLEM CATEGORIES

SupervisedLearning

UnsupervisedLearning

COMMON PREDICTIVE ANALYTICS METHOD

• Regression:

Predicting output variable using its cause-effect relationship with input variables. OLS Regression, GLM, Random forests, ANN etc.

• Classification:

Predicting the item class. Decision Tree, Logistic Regression, ANN, SVM, Naïve Bayes classifier etc.

• Time Series Forecasting:

Predicting future time events given past history. AR, MA, ARIMA, Triple Exponential Smoothing, Holt- Winters etc.

COMMON PREDICTIVE ANALYTICS METHOD (CONTD.)

• Association rule mining:

Mining items occurring together Apriori Algorithm.

• Clustering:

Finding natural groups or clusters in the data. K-means, Hierarchical, Spectral, Density

based EM algorithm Clustering etc.

• Text mining:

Model and structure the information content of textual sources. Sentiment Analysis,

NLP

BUSINESS APPLICATIONS OF PREDICTIVE ANALYTICS

Factory Failures

Finance Smarter HealthcareMulti-channel

sales

Telecom

Manufacturing

Traffic Control

Trading Analytics Fraud and Risk

Renewable Energy

Spam Filters

Retail: Churn

PREDICTIVE ANALYTICS TOOLS IN MARKET

LEARNING LINKS

• https://www.coursera.org/browse/data-science

• http://www.kdnuggets.com/2015/09/15-math-mooc-data-science.html

• http://www.datasciencecentral.com/profiles/blogs/how-to-become-a-data-scientist-for-free

• http://www.r-bloggers.com/in-depth-introduction-to-machine-learning-in-15-hours-of-expert-videos/

• http://online.stanford.edu/course/machine-learning-3

• http://online.stanford.edu/course/mining-massive-datasets

• Kaggle : The leading platform for predictive analytics competitions

• https://www.analyticsvidhya.com/

QUESTIONS AND DISCUSSION

THANK YOU SO MUCH FOR YOUR VALUABLE TIME

Mitesh GuptaEmail: mitesh.gupta19@gmail.com

LinkedIn: https://in.linkedin.com/in/mitesh-gupta-28014633

07869596947