Internet of Things, IoT

1. 1Copyright © 2014 Tata Consultancy Services Limited
A ubiquitous platform for IoT - bridging
the gap between the sensor world and
the application world
Dr. Arpan Pal
Principal Scientist, Innovation Lab, Tata Consultancy Services Ltd., India
17 May 2015

2. 2
Tata Consultancy Services (TCS) at a Glance
Bangalore, India1
Chennai, India2
Cincinnati, USA3
Delhi, India4
Hyderabad, India5
Kolkata, India6
Mumbai, India7
Peterborough, UK8
Pune, India9
2000+ Associates in Research, Development and Asset Creation
1 2
3
4
597
6
8
10
Singapore10
Innovation @ TCS
 Pioneer & Leader in Indian IT
TCS was established in 1968
 One of the top ranked global software service provider
 Largest Software service provider in Asia
 300,000+ associates
 USD 15Billion+ annual revenue
 Global presence – 55+ countries, 119 nationalities
 First Software R&D Center in India

3. 3
Internet-of-Things – at the peak of the Hype?

4. 4
Internet-of-Things – what does it really mean?
M2M Communication
Sensing the human – quantified self
Embedded software
and Hardware
Cloud, Mobile, Big Data
and Analytics
Wireless Sensor Networks,
Pervasive Computing
Sensors
and Actuators

5. 5
The Internet of Everything
Humans
Physical
Objects and
Infrastructure
Computing
Infrastructure
Physical
Context
Discovery
INTERNET OF EVERYTHING
Physical Context
Discovery
What is happening, where
and when
People Context
Discovery
Who is doing what, where
and when, who is thinking
what
Internet
of
Digital
Internet
of
Things
Internet
of
Humans
ABI Research. May 7, 2014
• New Business / Pricing Models
• Customer becomes the focus, not the
product or service – key is understanding
the Customer

6. 6
Platform Requirements for IoT
TCS Connected Universe Platform (TCUP)
A horizontal platform for addressing the IoT Software and Services market
Applications need support for
Visibility
Capture & store data from
sensors
Insights
Patterns, relationships and
models
Control Optimize and actuate
TCUP Platform

7. 7
TCUP Architecture
Sensor
Services
Sensor
Registration /
Describe
Sensor
Get Sensor
Capabilities
Insert
Observation
Get
Observation
Device
Management
Edit
configuration
Update
device
firmware
Download
software and
remote status
monitoring
Alarms and
notification
Analytics
Framework
Register Job
Deploy /
Undeploy Job
Get Job
Status
Start / Restart
/ Stop Job
Search Job
Flexible Interfaces for easy application development and integration
Adopt Open-source and Open Standards
TCUP API Classes
APPhonics
Develop– Test - Publish - Manage
People Things
TCUPKnome
DevelopersDevelopers

8. 8
Challenges for IoT Platform
Scalability
Privacy
Affordability
Context-awareness
Ease-of-Development
Security
S
A
E
C
P
S
Analytics is
the Key

9. 9
IoT Analytics – what does it really mean?
http://www.ciandt.com/card/four-types-of-analytics-and-cognition

10. 10
Analytics-as-a-Service
Algorithm
Recommendation:
Ease-of-
Development and
Fusion
Analytics Libraries: Affordability via Reuse and
Knowledge Modeling
Base TCUP Platform (Sensor Data Transport, Storage and Analysis)
Scalability and
Affordability:
Utilize Edge
Devices –
seamless
distributed
computing
(Fog)
Prescriptive DescriptiveDescriptiveDescriptivePredictive Diagnostic

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Sensor-agnostic Anomaly Detection – Remote Health Monitoring
Sensed data –
PPG, ECG, HR,
BP, Heart Sound,
Smart-Meter …..
Outlier
Detection
Information
Measure
Generate
Alerts based
on critical
information
Preventive
Healthcare
Promote WellnessSensor agnostic outlier
analysis library
Refer to Doctors
Being Tested on ECG, PPG and EEG Data
• Anomaly within same source, same time
• Anomaly within same source, different time
• Anomaly between different sources
• Knowledge model – sensor data type
dependency for outlier algorithms

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Behavior Sensing – Crowd sourcing of people context using mobile phones
Indoor Localization – Bldg, Mall
• Entry-Exit and Zoning
• Fine-grained positioning
Activity Detection - Wellness
• Walking / Brisk Walking / Jogging / Running
• Calorie Burnt
Traffic Sensing – City Authority
• Congestion Modeling
• Honk Detection
• Road Condition Monitoring
Driving Behavior - Insurance
• Hard Cornering / Breaking
Magnetometer –
Entry/Exit
WiFi -Zoning Bluetooth -
Proximity
RFID
Fusion
98% 97% 96% 99.7%
(Accuracy ~2m)
(Accuracy ~ 98%)
Mobile phone sensors – Magnetometer, Wi-Fi,
Bluetooth, Accelerometer, Microphone, GPS
Knowledge – Sensor Noise Models, physical world
models is form of building plan, road maps, driver-
vehicle interaction models

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Measurement – using Camera Vision for Physical World Metrics
eGarment Fitting – Online Retail
• Web cam based affordable system at home
• Real-time 3D reconstruction is a challenge
Accident Damage Assessment - Insurance
• Mobile phone camera based Insurance Application
• Template based damage assessment
Postal Packaging Automation - Online Resellers
• Mobile Camera based System
• Camera vision based approach
• 3D reconstruction from 2D images
• Affordable, quick to deploy systems
Sensors - Mobile Phone Camera, Webcams
Knowledge – Physical Object 3D Models (Human, Car, Box)

14. 14
Vision: Democratizing IoT App Development
I only know the business
logic, I do not know how to
code, nor do I understand
analytics algorithms…
I know how to code, but I do
not know algorithms, nor do I
know about the business
logic…
Oh, I know algorithms, but
I can’t code for your
mobile devices…
I have all these cloud and
edge nodes which you can
use to deploy the app…
Need of the Day - Knowledge-driven Framework for IoT App Development

15. 15
Model-driven-development for IoT – Separation of Concerns through Knowledge
Modeling
• Knowledge models include rules, ontologies, Information flow graphs, physical models
• Ratified / Augmented by experts (domain, sensor, algorithm and infrastructure)

16. 16
Publication List
Anomaly Detection and Compression
1. A Ukil, et. al., “Adaptive sensor data compression in IoT systems: sensor data analytics based Approach”, ICASSP 2015
2. One more
Crowd-sensing via Mobile Phones
1. Nasimuddim Ahmed et. al., ""SmartEvacTrak: A People Counting and Coarse-Level Localization Solution for Efficient
Evacuation of Large Buildings“, CASPER'15 workshop of IEEE Percom 2013
2. Sourjya Sarkar et. al. “Improving the Error Drift of Inertial Navigation based Indoor Location Tracking” , IPSN 2015
3. Vivek Chandel et.al., "AcTrak - Unobtrusive Activity Detection and Step Counting using Smartphones“, Mobiquitous 2013
4. Ghose, Avik et. al., "Road condition monitoring and alert application: Using in-vehicle smartphone as internet-connected
sensor.“, Percom Workshops 2012.
5. Tapas Chakravarthy et. al., “MobiDriveScore — A system for mobile sensor based driving analysis: A risk assessment model for
improving one's driving”, ICST 2013
6. Maiti, Santa, et al. "Historical data based real time prediction of vehicle arrival time." ITSC 2014
3D Vision based Measurements
1. Saha, Arindam et. al.,"A System for Near Real-Time 3D Reconstruction from Multi-view Using 4G Enabled Mobile." IEEE
Mobile Services (MS), 2014
2. Brojeshwar Bhowmick et. al., “Mobiscan3D: A low cost framework for real time dense 3D reconstruction on mobile
devices”, IEEE UIC 2014
Model-driven Development
1. A. Pal et al., “Model-Driven Development for Internet of Things: Towards Easing the Concerns of Application Developers,” IoT
as a Service (IoTaaS), 2014
2. S. Dey et al., “Challenges of Using Edge Devices in IoT Computation Grids,” ICPADS 2013
IoT Platform
1. P. Balamuralidhara et al., “Software Platforms for Internet of Things and M2M,” Journal of. Indian Inst. of Science
2. www.tcs.com/about/research/Pages/TCS-Connected-Universe-Platform.aspx - TCUP Platform Page

17. 17
Thank You
arpan.pal@tcs.com