IOT and Big Data - The Perfect Marriage

Copyright©RIOT2015AllRightsReserved

INTERNET OF THINGS & BIG DATA THE PERFECT MARRIAGE

Dr. Mazlan Abbas CEO, REDtone IOT Sdn Bhd

International Conference on Soft Computing in Data Science 2015 (SCDS2015), 2-3 Sept. 2015, Pullman, Putrajaya


THE PERFECT MARRIAGE •  Introduction - Internet of

Things and its Business Opportunities

•  Making Sense of Data •  Encourage Adoption and

Innovation of IoT •  IOT and Big Data Marriage

Challenges •  Summary


How Big is the IOT Market?


HOW IS IT DIFFERENT FROM M2M?

THE CURRENT ERA OF IOT


CHANGE IN BUSINESS MODEL


[Source: http://postscapes.com/what-exactly-is-the-internet-of-things-infographic ]


10/90 RULE The Last 100 meter

connectivity

The “last 100 meters” represent > 90% potential number of connections

Today, the devices used in the “last 100 meters” are typically not connected. The wide-area network is to a larger extent connected e.g. through smartphones, home routers (e.g. ADSL routers) and GSM / 3G / 4G Routers.

Still Disconnected Connected World


FUTURE OF COMMUNICATIONS AND PRODUCTIVITY

BRING IOT TO THE NEXT LEVEL


Improved Performance

Reduced Costs

Create Innovative Products

New Revenue Streams

Monitor

Autonomous

Optimize

Control

BUSINESS OPPORTUNITIES Capabilities Benefits


Security and Privacy of data

Managing vast amounts of data

Fragmented industry

Finding the right business model

WHAT ARE THE BUSINESS CHALLENGES


WHY TELCOS HAVE DIFFICULTY TO EMBRACE IOT

4 REASONS


LEGACY BUSINESS IS STILL CONNECTIVITY

M2M HIGH VOLUME LOW ARPU

SIM-CARD BASED

OLD HABITS DIE HARD

LESS COMPLEX


LACK OF DOMAIN EXPERTISE

THINK BEYOND CONNECTIVITY

SOLUTIONS, SELLING AND MANAGED SERVICES

NEED GO-TO-MARKET PARTNER


MINDSET BLOCK

FULL OF SKEPTISM

LOW SINGLE DIGIT GROWTH CONTRIBUTION


STABLE BUSINESS VS. RISKY VENTURES

NEED TO WORK LIKE STARTUP

IOT BUSINESS IS FLUID


END-TO-END IOT SERVICE PROVIDER

THE END GAME


s m a r t c i t y

smart transport

s m a r t a u t o m o t i v e

END-TO-END IOT SERVICE PROVIDER

Device Provider

Network Provider

Platform Provider

Application Provider

Customer

Network Platform


CONNECTIVITY PLAYER PHASE-1


s m a r t c i t y

smart transport


BUSINESS MODEL

Device Provider

Network Provider

Platform Provider


Customer

Network

Platform

Platform

1


IOT ENABLER PLAYER PHASE-2


s m a r t c i t y

smart transport


BUSINESS MODEL

Device Provider

Network Provider

Platform Provider


Customer

Network Platform

2


s m a r t c i t y

smart transport


BUSINESS MODEL

Device Provider

Network Provider

Platform Provider


Customer

Network Platform

IOT Subsidiary

3


IOT SOLUTIONS PLAYER PHASE-3


s m a r t c i t y

smart transport


BUSINESS MODEL

Device Provider

Network Provider

Platform Provider


Customer

Network Platform

4


IOT SERVICE PROVIDER PHASE-4


s m a r t c i t y

smart transport


BUSINESS MODEL

Device Provider

Network Provider

Platform Provider


Customer

Network Platform

5


s m a r t c i t y

smart transport


BUSINESS MODEL -HYBRID

Device Provider

Network Provider

Platform Provider


Customer

Network Platform

6


MAKING SENSE OF SENSOR DATA … BUT WHAT DATA?

THE GOLD RUSH


TO OVERCOME 3 KEY CHALLENGES

Only by addressing all three can organizations turn raw data into information and actionable insights.

Integrating data from multiple sources

Automating the collection of data

Analyzing data to effectively identify actionable insights


Wisdom

Knowledge

Information

Data

More Important

Less Important

Evaluated understanding

Appreciation of

Answers to questions.

Symbols

Understanding

Answers to questions

WHO

WHY

HOW

WHAT

WHERE WHEN

VALUE IS CREATED BY MAKING SENSE OF DATA

VALUE PYRAMID


Wisdom

Knowledge

Information

Data

More Important

Less Important

N/A

Empty (0), Full (1)

Understanding

EXAMPLE SMART PARKING

Who park at this lot? What kind of vehicle?

Where is the empty parking lot? When is the peak period?

How to implement a tiered charging? How to find “overstayed” vehicles?

Why this parking area is not fully occupied?

Who Benefits? - Citizens / Parking Operators / City Council / Shops


Home Health Transport Office Waste

DATA BLENDING

Creating New Compound Applications


All personal items, such as mobile phones, wrist watches, spectacles, laptops, soft drinks, food items and household items, such as televisions, cameras, microwaves, washing machines, etc

Private business organization has the right to take the decision whether to publish the sensors attached to those items to the cloud or not.

Public infrastructure such as bridges, roads, parks, etc. All the sensors deployed by the government will be published in the cloud depending on government policies.

Business entities who deploy and manage sensors by themselves by keeping ownership. They earn by publishing the sensors and sensor data they own through sensor publishers.

Personal and Households

Commercial Sensor Data

Providers

Organizations

Public Private

[Source: “Sensing as a Service Model for Smart Cities Supported by Internet of Things”, Charith Perera et. al., Transactions on Emerging Telecommunications Technology, 2014]

CHALLENGES – DATA OWNERSHIP


ENCOURAGE ADOPTION AND INNOVATION OF IOT

HOW-TO


“Makers, startups and crowdsourcing efforts result in high numbers of low-revenue niche IoT applications.”


WHERE ARE THE IOT DEVELOPERS?

[Source: http://arc.applause.com/2015/07/27/internet-of-things-big-data-and-analytics/]


SENSING-AS-A-SERVICE WHAT-IF


The city would pay for access to the light sensors in order to decide when to turn on and off the street lights

Gathering temperature, light, pressure, humidity and

pollution.

COMMERCIAL IOT SENSOR PROVIDER

A university may want access to the pollution information for research purposes for a limited period

The weather department would want the temperature and pressure data

The street town council center would want the temperature and humidity data for planning during rough weather


HARNESSING THE CREATIVITY


PARTICIPATORY SENSING – “RAPID DEPLOYMENT”


REDUCTION OF DATA ACQUISITION COST – “SUSTAINABLE BUSINESS MODEL”


DIFFERENT BUSINESS MODELS


COLLECT DATA PREVIOUSLY UNAVAILABLE – “ASSIST SCIENTIFIC OR SURVEY ACTIVITIES”


CROWDSENSING GETTING INSIGHTS FROM

EMPOWER THE CITIZENS TO SENSE


SMARTPHONE AS YOUR “SENSING ASSISTANT” Sensors: ①  Camera – “Eyes” ②  Audio – “Ears” ③  Accelerometer –

“Speed” ④  GPS – “Location” ⑤  Gyroscope –

“Movement” ⑥  Compass – “Direction” ⑦  Proximity –

“Closeness” ⑧  Ambient light – “Eyes” ⑨  Others…

Crowdsourcing Via Crowdsensing Context ①  Spatial – Location / Speed Orientation ②  Temporal – Time / Duration ③  Environmental – Temperature / Light / Noise Level ④  User Characterization – Activity (Mobility Pattern) / Social (Friends, Interactions)


MAKING CITIES BETTER USING CITIZENS

Traffic

Noise Environment

Network Coverage (WiFi/3G/4G)


LET ALL CITIZENS BE OUR “EYES”


Incident reporting facilities - citizens can report on issues concerning public infrastructure allowing collective collaboration to ensure an active response

COLLECTIVE COLLABORATION WITH CITIZENS


MOBILE APPLICATION

LIVABILITY LOCAL AREA SERVICES

PUBLIC SAFETY NATURAL DISASTER

Open Data

Smartphone Users

Social Media

Users

PORTAL CITISENSE.COM

CASE MANAGEMENT SYSTEM

DASHBOARD

EMPOWERING SMART CITIZENS



WHICH CAME FIRST – THE CHICKEN OR THE EGG?


SMART CITIZENS AND SENSORS CONNECTING AND AGGREGATING


Smart City

Environmental Monitoring

Multiple Sensors

Outdoor Parking Management

Parking sensors

Mobile Environmental

Monitoring Sensors installed in

public vehicles

Traffic Intensity Monitoring

Devices located at main entrance of

city

Guidance to free parking lots

Panels located at intersections

Smart Citizen Crowdsensing

Parks and Gardens Irrigation

Sensors in green zones

•  Temperature •  CO •  Noise •  Car Presence

•  Ferromagnetic sensors

•  Temperature •  CO •  Noise •  Car

Presence

•  Measure main traffic parameters •  Traffic volumes •  Road occupancy •  Vehicle speed •  Queue Length

•  Taking information retrieved by the deployed parking sensors in order to guide drivers towards the available free parking lots

•  Moisture temperature •  Humidity •  Pluviometer (rain gauge) •  Anemometer (wind-speed)

•  User generated feedback with smartphones that help to make cities better

Copyright©RIOT2015AllRightsReserved[Source: http://inrix.com]


IOT AND BIG DATA MARRIAGE CHALLENGES

IOT DEMAND A NEW WAY THINKING


CHALLENGE #1

FLOOD MONITORING


PREDICTING FLOOD 1.  What is the water level in the river? 2.  When does the river swell up? 3.  Where is the location of water rise? 4.  Where are the areas that require immediate evacuation? 5.  Who is contributing to the rise in water level? 6.  How to control the water level? 7.  How fast is the water rising? 8.  Why is the water rising?


FLOOD – DIFFERENT SENSORS 1.  Water level and weather sensors 2.  Tide monitoring sensor 3.  Astronomical data 4.  GPS for rescue vehicles (Plug-n-Play OBD) 5.  GIS data 6.  Camera 7.  Social network integration


FLOOD MONITORING – VALUE PROPOSITION 1.  Early warning system for floods 2.  Locating the area of violation (cause of floods) 3.  Efficient rescue planning 4.  Plan the widening of the river banks 5.  Plan deepening of the river bed 6.  Predict the conditions of flooding when coupled with a weather

station, astronomical, tide and GIS.


CHALLENGE #2

RIVER MONITORING


MONITORING RIVER POLLUTION 1.  What are the pollutants in the river? 2.  What is the pollution level? 3.  When is the highest pollution detected? 4.  Where is the location of pollutants? 5.  Who is contributing to the rise in pollution? 6.  How to control the pollution level? 7.  How much is the pollution level? 8.  Why is the pollution rising at certain time? 9.  Why is the pollution rising at certain locations?


RIVER MONITORING - SENSORS 1.  Chemical sensors 2.  Water level sensors 3.  Crowd sourced violation information (mobile device) 4.  GPS input 5.  Hydro morphology e.g. depth, width, flow 6.  Water temperature sensor 7.  In-situ (manual sampling) 8.  Weather sensors


RIVER MONITORING – VALUE PROPOSITION 1.  Early warning system for water contamination 2.  Locating the area of violation 3.  Detecting and alerting violations 4.  Getting public involvement through crowd sourcing 5.  Evidence platform for violations 6.  Identify potential causes of pollution rise by correlating data with

water level pattern 7.  Identify a profile of water in different months of the year. Any

change in profile will trigger external sources of pollution 8.  Identify the spread of disease 9.  Ecotourism planning


Tons of interesting data

If you can’t take action on the data being collected, it’s likely not worth collecting, storing and analyzing.

Is it actionable for my business?


The Internet of Things and Big Data are two sides of the same coin, and building one without considering the other is a recipe for doom.


THANK YOU @REDtoneIOT REDtoneIOT

Presentations & Public Speaking

IOT and Big Data - The Perfect Marriage