1 Copyright © 2014 Tata Consultancy Services Limited

A ubiquitous platform for IoT - bridging the gap between the sensor world and the application worldDr. Arpan PalPrincipal Scientist, Innovation Lab, Tata Consultancy Services Ltd., India

April 18, 2023

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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

5976

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

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

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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

Sensorsand Actuators

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The Internet of Everything

Humans

Physical Objects and Infrastructu

re

Computing Infrastructu

re

Peo

ple

Con

text

Dis

cove

ry

PhysicalContext Discovery

INTERNET OF EVERYTHING

Physical Context

DiscoveryWhat 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

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Platform Requirements for IoT

TCS Connected Universe Platform (TCUP)A horizontal platform for addressing the IoT Software and Services market

Applications need support for

VisibilityCapture & store data from sensors

InsightsPatterns, relationships and models

Control Optimize and actuate

TCUP Platform

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TCUP Architecture

Sensor Services

Sensor Registration / Describe

Sensor

Get Sensor Capabilities

Insert Observation

Get Observation

Device Manageme

nt

Edit configuratio

n

Update device

firmware

Download software

and remote status

monitoring

Alarms and notification

Analytics Framewor

k

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

API Play TCUPKnome

DevelopersDevelopers

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Challenges for IoT Platform

Scalability

Privacy

Affordability

Context-awareness

Ease-of-Development

Security

S

A

E

C

P

S

Analytics is the Key

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IoT Analytics – what does it really mean?

http://www.ciandt.com/card/four-types-of-analytics-and-cognition

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Analytics-as-a-Service

Distributed Computing Infrastructure for IoT (Fog Computing)

Analytics Algorithm Repository for IoT (Algopedia)

Planning Prognostics

Causal Analytics

Behavior Sensing Measurement Anomaly

Detection

Algorithm Recommendati

on: 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, GPSKnowledge – 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, WebcamsKnowledge – Physical Object 3D Models (Human, Car, Box)

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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

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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)

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Publication List

Anomaly Detection and Compression1. A Ukil, et. al., “Adaptive sensor data compression in IoT systems: sensor data analytics based Approach”,

ICASSP 20152. One more

Crowd-sensing via Mobile Phones3. 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 20134. Sourjya Sarkar et. al. “Improving the Error Drift of Inertial Navigation based Indoor Location Tracking” , IPSN

20155. Vivek Chandel et.al., "AcTrak - Unobtrusive Activity Detection and Step Counting using Smartphones“,

Mobiquitous 20136. Ghose, Avik et. al., "Road condition monitoring and alert application: Using in-vehicle smartphone as internet-

connected sensor.“, Percom Workshops 2012.7. Tapas Chakravarthy et. al., “MobiDriveScore — A system for mobile sensor based driving analysis: A risk

assessment model for improving one's driving”, ICST 20138. Maiti, Santa, et al. "Historical data based real time prediction of vehicle arrival time." ITSC 2014

3D Vision based Measurements9. Saha, Arindam et. al.,"A System for Near Real-Time 3D Reconstruction from Multi-view Using 4G Enabled

Mobile." IEEE Mobile Services (MS), 201410.Brojeshwar Bhowmick et. al., “Mobiscan3D: A low cost framework for real time  dense 3D reconstruction on

mobile devices”, IEEE UIC 2014

Model-driven Development11.A. Pal et al., “Model-Driven Development for Internet of Things: Towards Easing the Concerns of Application

Developers,” IoT as a Service (IoTaaS), 201412.S. Dey et al., “Challenges of Using Edge Devices in IoT Computation Grids,” ICPADS 2013

IoT Platform13.P. Balamuralidhara et al., “Software Platforms for Internet of Things and M2M,” Journal of. Indian Inst. of

Science 14.www.tcs.com/about/research/Pages/TCS-Connected-Universe-Platform.aspx - TCUP Platform Page

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Thank Youarpan.pal@tcs.com