This document summarizes Arpan Pal's presentation on communication and signal processing technologies for intelligent infrastructure and cyber-physical systems. It outlines the challenges of reducing communication costs, preserving battery power, and extracting information from noisy sensor data. It then provides examples of applying these technologies to intelligent transportation systems using accelerometer analytics on buses to analyze road conditions, home energy management through non-intrusive load monitoring of energy meter data, and mobile phone-based wellness applications using sensors to count steps, measure pulse, and classify activities. It also introduces RIPSAC, a generic platform developed by TCS for Internet of Things applications.

1. 1
Communication and Signal Processing Technologies for
Intelligent Infrastructure
Arpan Pal
Head of Research,
Cyber-physical Systems
Innovation Lab, Kolkata
22nd November, 2012
NCCCS 2012

2. Outline
Intelligent Infrastructure and Cyber-physical Systems
Architecture
Challenges
Intelligent Transportation
Accelerometer Analytics
Lightweight Protocols for IoT M2M Communication
Home Energy Management
Meter Data Disaggregation
Mobile phone base Wellness
Accelerometer Analytics
Mobile Camera Image Processing
RIPSAC – a generic platform for Internet-of-Things

3. Intelligent Infrastructure and Cyber-physical
Systems

4. 4
Signal
Processing
Architecture
Sense
Extract
Analyze
Respond
Learn
Monitor
Intelligent
Infra
@Home
@Building
@Vehicle
@Utility
@Mobile
@Store
@Road
“Intelligent” (Cyber) “Infrastructure” (Physical)
APPLICATION SERVICES
BACK-END PLATFORM
INTERNET
GATEWAY
Sense
Extract
Analyze
Respond
Communication

5. 5
Challenges
Challenges
• Reducing the cost of Communication
• Preserving the Battery power
• Extracting information from ambient-noise-corrupted sensor data
• Modeling the physics of the sensor for rich analytics
Solution Approach
• Edge processing (sensor, audio, image, video) for context extraction
• Lightweight communication protocols over Internet
• Cyber-physical system model-driven system identification for analytics

6. Intelligent Transportation

7. 7
Intelligent Transportation
M2m Cloud
Bus Tracking System
Location ( GPS), Speed, Accelerometer, Passenger
Ids
Valid passenger lists, Route Info
• Can we map the Road Condition?
• Can we predict Vehicle Condition?
• Can we detect Bad Driving Behavior?
• How to send sensor data over internet preserving
battery power and reducing network overload?
Pilot at TCS, Siruseri Campus
Business Problems

8. 8
Vehicle Model Driven Sensor Data Analysis
CONSTANTS WE CAN MEASURE
Vehicle Type & Driving Behavior Road Condition Monitoring
Road Condition & Driving Behavior Car Prognosis
Road Condition & Vehicle Type Driving Behavior Analysis
Acceleration a(t) = f (H(t), v(t), R(t), D(t))
H(t)
System
Identification Tools

9. 9
Representative results : Analysis of Siruseri bus data
We compute “Road Roughness Index” for all the routes. For such evaluation, we assume basic bus
model. The analysis also detects potholes /bumpers using “Jerk”.
Significant events
Spectral Analysis
(10 sec window)
Computed
ISO Classification
Inference:
“GOOD ROAD”
“JERK” is computed
(100 sec window in the
picture) to identify
isolated events

10. 10
Validating simulation with measurements
Simulation is validated with Tata Nano car as it is forced to traverse potholes. The accelerations are
measured using SAMSUNG Galaxy Phone kept at fixed location inside the car.
Results show good match between computed pothole impact & the measured ones.. The sampling frequency is 20Hz.
The deviation is amplitude is due to the approximate estimation of the impact area for pothole-tire interaction.

11. 11
Optimized M2M communication
Enhancement & Optimization of communication cost
• Enhancement of network throughput with reliable communication.
• Bandwidth and energy usage optimization.
• Reduction of information content.
• Scalability and energy constrained are the main issues to address

12. 12
Broadcast Based Communication
Universal Compaction using lossless source coding
Farkas, P.; Halcin, F.; , "Communication techniques for wireless sensor networks using distributed universal compaction algorithms," Signal
Processing and Communication Systems, 2009. ICSPCS 2009. 3rd International Conference on , vol., no., pp.1-6, 28-30 Sept. 2009
Preservation of uplink bandwidth and sensor node power

13. Home Energy Management (HEM)

14. 14
Home Energy Management (HEM)
Utility
Appliances
Intelligent
Gateway
Smart Meter
Appliance Management
Consumption View
On-off Control
Social Network
Integration
Consumer Home
Pilot with a Greening Company in Netherlands
• How can we identify appliances and calculate their consumption from aggregate meter
data?
• How would home users share their data without being concerned about their privacy?
Business Problems

15. 15
Objective : Disaggregate the energy meter data to get operating status of appliances connected
Research Aspects:
– Measure active/reactive power, current/voltage waveforms using (1 Hz – 1/900 Hz) sampling rate
– Uniform smart meter data semantics by running an ontology engine - ease of data interpretation
– Pre-processing, probabilistic graphical models, temporal reasoning/data mining, pattern recognition
Non Intrusive Appliance Load Monitoring (NIALM)
Input Output

16. 16
NIALM leads to Privacy Breach

17. 17
Meter Data Privacy Management

18. Mobile Phone based Wellness

19. 19
Sensor Collector
Mobile phone based Wellness
• How do we log exact km.s run?
• How do we automatically classify the activity (Stationary, Walking, Brisk Walking,
Jogging, Spot Jogging, Running)?
• How do we accurately measure the calories burnt?
• How we take the instantaneous pulse rate?
Fit4Life – A wellness initiative within TCS
Business Problems
Accelerometer
Gyroscope
Compass
Camera

20. 20
Sensor Penetration and power consumption in Mobile
Phones
0 20 40 60 80 100
Bluetooth
USB
Edge
GPRS
Wifi
3G
Camera
GPS
Accelarometer
Digital Compass
Consolidated Market Penetration
Source: Nericell: Rich Monitoring of of Road and Traffic Conditions using Mobile Smartphones, Prashant Mohan et. al., Microsoft
Research, SenSys 2008, North Carolina, USA

21. 21
Step Count using Accelerometer in iPhone
Challenges
• Movement Noise Cancellation – Frequency-domain Approach
• Orientation Correction – Gyroscope and Compass based

22. 22
Subjects Subject1 Subject2 Subject3 Subject4
Use
Cases
Actual Detected Actual Detected Actual Detected Actual Detected
Avg . Err%
Hand 90 76 84 83 96 91 85 70 9.9%
Shirt
Pocket
90 90 86 86 93 85 88 88 2.1%
Trouser
Front
Pocket
90 84 85 90 95 96 89 92 4.24%
Trouser
Rear
Pocket
92 90 85 83 95 91 90 81 4.68%
Waist Clip 89 96 85 84 94 91 87 83 4.2%
Avg. Err% 6.4% 2.12% 4.45% 7.1% 5.024%
Next Steps
• Canceling effects of Hand Movement
• Activity Classification based on Step Count
• Calorie-Burnt Estimation using Activity
Step Count Results – contd..

23. 23
PPG based Pulse Measurement using Phone Camera
Challenges
• Movement Noise Cancellation – Correlation with Accelerometer
• Operating in low ambient light – Advanced video pre-processing
Subject1 Subject2 Subject3
Actual Detected Actual Detected Actual Detected
68 66 66 63 85 84
2.9% 4.5% 1.1%

24. 24
Generic Platform for IoT from TCS
Internet
End Users &
Renderers
Administrators
Device Integration & Management Services
Analytics Services
Application Services
Storage
Messaging & Event Distribution Services
ApplicationServices
Presentation Services
Application Support Services
OS & Device Drivers
Edge Middleware
Analytics Persistence
Application Services
Middleware(Security/PrivacyandProtocols)
Gateway
Sensors
Internet
Back-end
RIPSAC – Real-time Integrated Platform for Services & AnalytiCs

25. Innovation @TCS

26. 26
The Heart of Innovation – TCS Innovation Labs
Bangalore, India1
TCS Innovation Labs - Bangalore
Chennai, India2
TCS Innovation Labs - Chennai
TCS Innovation Labs - Retail
TCS Innovation Labs - Travel & Hospitality
TCS Innovation Labs - Insurance
TCS Innovation Labs - Web 2.0
TCS Innovation Labs - Telecom
Cincinnati, USA3
TCS Innovation Labs - Cincinnati
Delhi, India4
TCS Innovation Labs - Delhi
Hyderabad, India5
TCS Innovation Labs - Hyderabad
TCS Innovation Labs - CMC
Kolkata, India6
TCS Innovation Labs - Kolkata
Mumbai, India7
TCS Innovation Labs - Mumbai
TCS Innovation Labs - Performance Engineering
Peterborough, UK8
TCS Innovation Labs - Peterborough
Pune, India9
TCS Innovation Labs - TRDDC - Process Engineering
TCS Innovation Labs - TRDDC - Software Engineering
TCS Innovation Labs - TRDDC - Systems Research
TCS Innovation Labs - Engineering & Industrial Services
1 2
3
4
5
97
6
8
2000+ Associates in Research, Development and Asset Creation
19 Innovation Labs

27. 27
Academic Co-Innovation Network (COIN )
Fostering joint research
and innovation through a
mutually beneficial
alliance between TCS and
academia
Academic
context
Thoughts and research towards disruptive Innovation
Knowledge exchange and people development
Industry-oriented
Business context
innovation scalability of academia context of real-world problems
Collaborative
research
environment
Collaboration Measures Institution (H1 FY13)
Alliances Number of collaborations established and ongoing ISI, IIT-K, UCB, MIT, SMU (7)
Number of research alliances under consideration IISc, IITB (2)
Sabbaticals Sabbaticals from Academia Various Univs in India and Abroad (17)
Sabbaticals from TCS to Academia IIT D, Aalborg Denmark, BIT Mesra, IIT Kgp,
Oxford University, Bond University (8)
RSP Research Scholars supported under RSP scheme 29 top institutes -IITs, NITs, IISc, TIFR, IIITs
(100 )
IPR Papers,, Patents ISI , UC Berkley (15)

28. Thank You
arpan.pal@tcs.com