This document discusses using grid computing techniques to support internet-of-things (IoT) applications. It describes how edge devices can participate in a grid to distribute computing loads for IoT analytics. Example use cases are presented for home energy management and remote healthcare. Challenges include efficient communication between devices and balancing resource usage. Solutions involve agent-based distributed computing and lightweight protocols like CoAP. TCS's RIPSAC platform is presented as an IoT platform integrating these techniques.

1. 0Copyright © 2011 Tata Consultancy Services
Limited CSI Eastern Regional Convention, CVRCE Bhubaneswar, 25th Feb 2013
Grid Computing for Internet-of-Things
- towards Intelligent Infrastructure
Arpan Pal
Principal Scientist and Research Head
Innovation Lab, Kolkata
Tata Consultancy Services
With Arijit Mukherjee and Soma Bandyopadhyay
Innovation Lab, Kolkata

2. Outline
Innovation@TCS
Ubiquity and Internet of Things
Grid Computing for IoT
Example Use Cases
Challenges and Solution Approach

3. 2
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

4. 3
Innovation@TCS – the Co-Innovation Network
Ecosystem of innovative partners
encompassing:
 Academic Institutions
 Start up companies
 Government
 Service Providers
 Industry Bodies
Need : A rich and diverse network that drives innovation in an open
community:
Research
Labs
Startups
End Users
Research
Institutions
Academic
Institutions
Student
Community
Individuals
Service Providers
Government
departments
Industry BodiesUtilities

5. Ubiquity and Internet of Things
- towards Intelligent Infrastructure

6. 5
Ubiquitous Computing
“Ubiquitous computing enhances computer use by making many computers available
throughout the physical environment, but making them effectively invisible to the user”
At present Ubiquity is viewed as a Consumer phenomenon – However
widespread adoption of ubiquitous devices among Enterprise stakeholders will
drive Enterprises to ubiquity.

7. 6
Signal
Processing
Internet-of-Things - towards Intelligent Infrastructure
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
Computing

8. 7
Integrated Platform for Intelligent Infrastructure
People Feedback & Emotions
Social Media
Integrated Services
Sensors & IoT
Platform
Traditional Monitoring & Control
Systems
Citizen Data
Smart Integration Platform
Transportation Healthcare Electricity
WaterPublic Safety Tourism
Smart Integrated Services
Sense
Analyze
Extract
Respond
Intelligence
Smart Domain Services
Community
etc.
Sense: People Activity, Appliances, Vehicles , Road, Home/Bldg, Utility
Infrastructure
Detect gas leakage/water contamination :
mobilize rescue team, suggest optimum route
Divert Road Traffic in case of
Water Pipeline Burst
Correlate Electricity/Water
/Gas consumption patterns
Intelligent Integration Platform
Integrated Intelligent Services

9. 8
Technology Landscape
Intelligent
Infrastructure
Optimize
Digitize
Analyze
De-Risk
Sustain
Analytics-led transformation
Resource Optimization
Mobile Applications; Social Media, Digital
Consumer; M2M communication
Sensor Webs
Information Fusion
HPC, HTC, HFC and Big Data
Algorithms and Decision Sciences
Real-time Response
Security; Privacy vs. Utility, Trust
Green IT; IT for Green; Water
Healthcare

10. Grid Computing for IoT

11. 10
The Grid
“Resource sharing & coordinated problem solving in dynamic,
multi-institutional virtual organizations”
• Ian Foster, Grid Computing in Canada Workshop, University of Alberta, May 1, 2002

12. 11
 Software enables:
 On-demand access to services
 Secure, reliable, dynamic federation
 Definition & execution of workflows
 Applications:
– Address complex problems
– Provide community services
 Facilities:
– Provide access to resources
– Host robust services and content
Grid Technology
• Ian Foster, Chicago Technology Forum, October 28, 2005
• Min-Jen Tsai, ,Yuan-Fu Luo , Expert Systems with Applications, Volume 36, Issue 7, Sept. 2009,
Elsevier

13. 12
Grid Computing and IoT
 It is all about Intelligent Systems
 Intelligence comes from Analytics
 Need for crunching huge amount of sensor data and
respond in real-time
 Needs huge computing infrastructure in cloud
 Another option is to distribute computing load to the edge
devices

14. 13
The Grid in IoT is in the Edge - Fog Computing
• Flavio Bonomi et.al. MCC2012, Helsinki, Finland

15. 14
Advantages
 Edge Devices computing power remain unused most of the
time
o Free Computing resource for the grid
o Potentially millions of ~1GHz Processors on the grid depending upon
use case
 Energy cost at edge is typically at consumer rates <<
Energy cost at cloud which is at Enterprise rates
o Energy cost account for 50% of Data Center Opex

16. Example Use Cases

17. 16
Utility
Appliances
Smart
Plugs
Intelligent
Gateway
Smart
Meter
Demand Forecasting
Demand Response
Appliance Management
Consumption View
Appliance Scheduling
On-off Control
Social Network
Integration
Consumer Home
Analytics
Home Energy Management
RIPSAC

18. 17
Healthcare – Remote Medical Consultation
ECG
Body Fat Analyzer
Blood Pressure
Monitor
Pulse OxyMeter
Mobile gateway
Patient
Records
Health Center / Home
Expert Doctor
Analytics
and
Decision
Support
Systems
Wireless gateway

19. Challenges and Solution Approach

20. 19
Challenges
• Using the Internet as the media for distributing data to the
edge device will cost the edge device owner and use battery at
edge device
• How to reduce the cost of Communication
• How to preserve the Battery power
• Edge device should be used only during its idle time and
should not effect the user experience during its normal usage
• How to sense idle time in real-time and allocate job / distribute
data optimally
• Smartphones as edge devices
• Incentivisation for users to allow this
• Edge devices are typically constrained in memory and have
variety of hardware and software flavors
• Need to factor in device capability in job scheduling design
• Need to create common middleware framework for job
distribution / execution

21. 20
Solution Approach – Computing Aspect
• Agent-based grid Computing using CONDOR
• Need for agents in diverse types of edge devices via a common framework
• Min-Jen Tsai, ,Yuan-Fu Luo , Expert Systems with Applications, Volume 36, Issue 7,
Sept. 2009, Elsevier

22. 21
Solution Approach – Communication Aspect
HTTP is heavy-weight.
CoAP is the most efficient in terms of bandwidth.
Power usages by CoAP and HTTP to send GPS
data using mobile phone as sensor gateway
Scenarios cover indoor and outdoor with increasing
mobility
• http://people.inf.ethz.ch/mkovatsc/californium.php
• Ralf Koetter, Muriel Medard, 2003 IEEE/ACM transaction http://web.mit.edu/medard/www/NWCFINAL.pdf
• Soma Bandyopadhyay, Abhijan Bhattacharyya, Workshop on Cyber Physical Systems (CPS), 2013 International Conference
on Computing, Networking and Communication (ICNC, 2013)
COAP – the Constrained Object Access Protocol

23. 22
Broadcast Based Communication – the Future?
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

24. 23
IoT Platform from TCS
Internet
End Users
Administrators
Device Integration & Management Services
Analytics Services
Application Services
Storage
Messaging & Event Distribution Services
ApplicationServices
Presentation Services
Application Support Services
Middleware
Edge Gateway
Sensors
Internet
Back-end on Cloud
RIPSAC – Real-time Integrated Platform for Services & AnalytiCs
Traditional
Internet
 Service Delivery
Platform & App
Development
Platform
 Security/Privacy
Framework
 Lightweight M2M
Protocols
 Analytics-as-a-
Service
 Social Network
Integration
 SDKs and APIs for
App developer
Grid
Computing
Components

25. Thank You
arpan.pal@tcs.com