Internet of Things, IoT

1. 1Copyright © 2014 Tata Consultancy Services Limited
Analytics as a Service for IoT
Dr. Arpan Pal
Principal Scientist, Innovation Lab
Tata Consultancy Services Ltd.
India
17 May 2015

2. 2
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

3. 3
Understanding the Physical Context
New Business / Pricing Models, Always On–Anytime–Anywhere, Secure, Context-aware - need
to guarantee ROI for sustainability
Enables real-time monitoring to
reduce downtime, reduce cost of
maintenance and improve personnel
safety, predicts wind-speed to
improve productivity
Enables crop scouting and mapping
of farmland to improve productivity
of the farmers

4. 4
Understanding the People Context
Non-intrusive, un-obtrusive sensing
Identity, Location, Activity, Physiology
Understand Behavior – Individuals /
Groups
Quantified Self
Customer becomes the focus, not the product or service – key is understanding the Customer,
Extend B2B to B2B2C

5. 5
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
Analytics is the
Key

6. 6
IoT Analytics – what does it really mean?
http://www.ciandt.com/card/four-types-of-analytics-and-cognition

7. 7
Challenges for IoT Analytics
Scalability – Distributed Computing
Affordability – Reusability
Fusion – Sensor Data and Error
Modeling
Ease-of-Development – Address
Complexity
S
A
E
F
 A sensor techie
 An embedded programmer
 A cloud programmer
 An algorithm expert
 A domain specialist
 An infrastructure expert
The App Developer needs to be

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Analytics-as-a-Service
Algorithm
Recommendation:
Ease-of-
Development and
Fusion
Analytics Libraries: Affordability via Reuse
Base TCUP Platform (Sensor Data Transport, Storage and Analysis)
Compute
Scalability:
Utilize Edge
Devices
Prescriptive DescriptiveDescriptiveDescriptivePredictive Diagnostic

9. 9
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)

10. 10
Proposed Architecture
Algorithm repository
TCS Connected Universe Platform
infrastructure sensors
Scheduler/Execution Engine
Analytic Service Layer
Workflow Engine Algorithm Recommender Partition Recommender
Knowledge Base
(Algorithm,
Infrastructure)
Planning Prognostics
Behavior
Sensing
Measurement
Anomaly
Detection
Applications
Domain and Sensor Knowledge Base
Causal
Analytics
Rule and Reasoning Engine

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Model-driven Framework for IoT Analytics

12. 12
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
• Can also be used for Adaptive Compression

13. 13
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
People web-behavior - Telecom
• Location-based clustering
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

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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 - Transportation
• 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)

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Other Analytics Services – Causal Analysis, Prognosis, Planning
Causal Analysis - Vehicular Telemetry
• Fault Detection - Automatic switch-over to
another sensor when one sensor fails
• Information flow graph based knowledge
modeling
• Telemetry Sensor data from OBD port
Prognosis – Remote Health Monitoring
• Knowledge Ontology from Web and experts
on Disease to Symptom to Sensor
Observation mapping
• Learning cum abductive reasoning based
inference to prognose disease from sensor
data
• Sensor data from Pathological and
Physiological Devices
Planning – Emergency Evacuation
• Knowledge in form of building floor plan
• Graph analytics based optimization
• Sensor data from BMS and Mobile phone
localization

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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

17. 17
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

18. 18
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
5
97
6
8
10
Singapore10
iCity Lab - Collaboration with Singapore Management University – Elderly Care, Mobile Sensing
46+
13.44
Billion US$ in Q1-FY15 revenues *
305,431
119
55+ Countries where TCS has presence
Employees*
Nationalities
Source:
Figures from TCS Analyst Report FY Q1-15
Employee count includes that of TCS subsidiaries
Years in Business
3.694
Billion US$ in FY14 revenues
Innovation @ TCS

19. 19
Thank You
arpan.pal@tcs.com