Internet of Things, IoT

1. 0Copyright © 2014 Tata Consultancy Services Limited ICDCN 2014, 6th Jan 2014
Harnessing the power of edge computing devices
for Real-time Analytics of IoT data
Dr. Arpan Pal
Principal Scientist and Research Head
Innovation Lab, Kolkata
Tata Consultancy Services
With Arijit Mukherjee, Himadri Sekhar Paul, Swarnabha Dey, Pubali Datta and Batsyan Das
Innovation Lab, Kolkata

2. Outline
Analytics in Internet of Things
Computing Requirements
Solution Approach – a Framework using
Distributed Computing on Edge Devices

3. Analytics in Internet-of-Things

4. 3
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

5. 4
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 Services & Analytics for Cyberphysical Systems
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

6. 5
Analytics Use Case - Home Energy Management
Source: IEE - Edison Institute, August 2013,
http://blog.opower.com/2013/09/report-smart-meters-in-us-now-generating-more-than-1-billion-data-points-per-day/
“Smart meters in US now
generating more than 1
billion data points per
day”

7. 6
Analytics Use Case - Remote Patient Monitoring
In 2012, worldwide digital healthcare data was estimated to be equal to 500 petabytes and is expected to
reach 25,000 petabytes in 2020.
Hersh, W., et. al. (2011). Health-care hit or miss? Nature, 470(7334), 327.
http://medcitynews.com/2013/03/the-body-in-bytes-medical-images-as-a-source-of-healthcare-big-data-
infographic/

8. 7
Experience certainty.
Analytics Use Case - 3D Reconstruction with 2D images from
mobiles
• Low cost solution for 3D reconstruction from multiple 2D images captured from
mobile device.
• Derive the motion information from the inbuilt sensors of the mobile phone and then
aid in increasing the accuracy of the 3D reconstruction.
Applications
• Agro-advisory Service
• Remote Diagnostics of Machines
• Remote Healthcare
Take pictures of a
heterogeneous object
from different angles
using mobile camera.
Extract the camera
parameters from the
captured images.
Reconstruct the object
using extracted camera
parameters.
Dense reconstruction - 0.5 million (approx. ) cloud points from 150 images (5 MP) - 8 minutes on 16 core CPU

9. Computing Requirements

10. 9
Grid Computing for IoT
 Intelligent Systems - Intelligence comes from Analytics
 Need for crunching huge amount of sensor data and
respond in real-time
 Needs humongous computing infrastructure in cloud with
dynamic load varying from application to application
 Another option is to distribute computing load to the edge
devices like mobile phones

11. 10
The Grid in IoT is in the Edge - Fog Computing
Source: Flavio Bonomi et.al. MCC2012, Helsinki, Finland
• Need to have economies of scale compared to traditional cloud

12. 11
At What Cost?
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
Issues
 End-users incur cost for computing energy and data communication
 Security and Privacy
 Battery Depletion
 What is the Incentive for the end-user

13. Solution Approach – a Framework for
Distributed Computing on Edge Devices

14. 13
Using Condor based Job Scheduling and Data Partitioning
“Utilising Condor for Data Parallel Analytics in an IoT Context - an Experience Report”, Arijit Mukherjee et. al., 9th IEEE
International Conference on Wireless and Mobile Computing, Networking and Communications, Workshop on the
Internet of Things Communications and Technologies (IoT 2013)

15. 14
Data Partitioning - Static
HugeDataSet
Analytics
Result
Data
Parallel
Analysi
s
Processing
Infrastructure P? How to partition the input data set when
 The computing nodes are heterogeneous (memory, CPU)
 They are not always available
D
R. Arasanal and D. Rumani, “Improving MapReduce performance through complexity and performance based data placement
in heterogeneous Hadoop clusters”, In Intl Conf. on Distributed Computing and Internet technology (ICDCIT), Feb 2013.
A Banerjee, A Mukherjee, H S Paul, S Dey, “Offloading work to Mobile Devices: An availability-aware data partitioning
approach”, In Proc of Middleware for Cloud-enabled Sensing (MCS), Dec 2013.

16. 15
Using Edge Devices - Detailed Framework Architecture
 Use edge devices like mobile phones as computing nodes especially
when they are connected to chargers and are idle
Mustafa Arslan et. al., “Computing While Charging: Building a Distributed Computing Infrastructure Using Smartphones”, In
CoNEXT’12, December 10–13, 2012, Nice, France.
Felix Büsching et. al/, “DroidCluster: Towards Smartphone Cluster Computing - The Streets are Paved with Potential
Computer Clusters”, 32nd International Conference on Distributed Computing Systems Workshops, 2012
 Need to have agents on edge
devices to find out their capability
and availability
 Need generic execution
framework on edge devices
 Need dynamic data portioning
algorithms based on sensed
capability and availability of edge
devices

17. 16
Solution Approach

18. 17
The Execution Engine - BOINC
Source: “Tapping the Matrix: Harnessing distributed computing resources using Open Source Tools”, Carlos Justininiano,
http://chessbrain.net/LFBOF2005/tappingthematrix.html
Anderson DP et. al,, “BOINC: a system for public-resource computing and storage”, Fifth
IEEE/ACM International Workshop on Grid Computing, 2004.
Berkeley Open Infrastructure for Network Computing

19. 18
Proposed solution on top of BOINC
 Agent on Edge Devices, Dynamic Data Partitioner,
Executable/Data/Result Transport Engine

20. 19
Results – I/O Intensive Text Search

21. 20
Results – Compute Intensive p Calculation

22. 21
Agent on Edge Devices - Exploiting unique usage pattern
9:00pm
11:00pm
8:00am
6:00pm
Idle slots
Data Tx/Rx
Wi-Fi signal
Screen state
App Category
CPU Idle
Cell signal
Memory free
A’s unique usage pattern
Apply mobile OS/architecture domain knowledge
To office by
bus
7:00pm
9:00am
9:00pm
11:00pm
8:00am
6:00pm
To office by
bus
7:00pm
9:00am
Parameters for identifying relatively free time periods
B’s unique usage pattern
Log
Sun Oct 27 01:21:40 IST 2013 --> 331 999960 true 31.0 -57.0 1.0 com.android.chrome
CPU  { Excellent, Good, Average, fair}
Memory  { High, Average, Low}
Signal { Excellent, Poor, Average}
Screen  { On, Off}
App  {High QOE, Background, Sporadic}
State S = { CPU X Memory X Signal X Screen X App }

23. 22
Ongoing and Future Work
 Automated dynamic sensing of edge device capability and
availability based on Edge Device Agent
– Improved dynamic data partitioner
 Addressing Security and Privacy
– Security issue of Personal Edge Devices allowing foreign executables
to run – Sand-boxing feature in BOINC
– Privacy issue of analytics on one users’ data happening on another’s
edge device – Need to build Trust models
 Energy depletion of battery powered devices
– Compute-while-charging
 Network congestion due to data movement
– Reduced overhead lightweight communication
 Incentivization of people donating their edge devices to the grid
– Bid based approach

24. Thank You
arpan.pal@tcs.com