An introduction to the Internet of Things (IoT) and its underpinning technologies and platform such as Cloud Computing and Big Data.

1. Internet of Things
Aniekan Akpaffiong
College Speaker Series
January 2014

2. Introduction
• Agenda
– Internet of Things/Everything
– Big Data
– Cloud Computing
– Virtualization

3. What Is Internet Of Things
The general concept of the Internet of Things is
that we can put a sensor on anything and have
it send data back to a database through the
Internet.
In this way we can monitor everything,
everywhere and build smarter systems that are
more interactive than ever before.
Dan Rowinski, article on ReadWrite.com

4. Google Trends
Virtualization
Cloud Computing
Big Data
Internet Of Things
Graph generated 2014

5. Where Are The Things

6. How Many Things
• Projections Vary
– Uses cases still largely theoretical
• Depends on what’s counted
– E.g. include smart phones
• Beware of motivations
– Defining markets, setting agenda
Example Estimates
2020 50 billion Cisco Systems, 2013
2020 26 billion Gartner

7. Internet of Things
• “Internet of Things”
– Popularized in 1999 by MIT research group
– Sensor networks & content tagging to enable
interaction with physical and logical objects
– Core technologies
• Nanotechnology, intelligent
embedded systems, RFID,
sensor technology,…
– Platform technologies
• IPv6, Big Data, Virtualization,
Cloud Computing
– Standards & APIs
Entrance to SARAH, the artificially
Intelligent "home of the future” in Syfy’s Eureka.

8. IoT Components
• Enablers/Core
– Wireless
– ZigBee/NFC/RFID
– MEMS/Nanotech
– Content Analytics
• Platform
– IPv6
– Virtualization
– Cloud Computing
– Big Data
• Objects
Smart Lock, August.com

9. Internet of Things
• Smart Planet (IBM), Planetary Skin (Cisco),
CeNSE (HP),…
• Connections, it’s not just of people anymore
• IOT is about
– Data
– Sensors
– Control
– Analytics
– Networks

10. Internet of Things
• Applications of IoT
– Manufacturing
• Robotics, analytics, smart meters
– Retail
• Inventory tracking,
– Healthcare
• Remote monitoring, records
– Transportation
• Autonomous vehicles, GPS
– Home
• Monitoring, SMART devices, locks
Thermostat, nest.com

11. Internet of Things
• Concerns
– Competence
– Technocracy
– Panopticon
– Profiling
– Hacking
– Complexity
– Inevitability
– Data Ownership
– Costs
– Energy disney.wikia.com
I’m not bad, I’m just drawn that way. – Jessica Rabbit

12. “If you have something that you don't
want anyone to know, maybe you
shouldn't be doing it in the first
place.” – Google CEO Eric Schmidt, December 2009 CNBC interview
Privacy & The Internet

13. Gartner Hype Cycle 2013
www.gartner.com/newsroom/id/2575515

14. Internet of Things

15. Virtualization
Hardware (CPU, RAM, HDD)
Host Operating System
App App
Before Virtualization
Hardware (CPU, RAM, HDD)
Hypervisor
App
Virtual
Machine #1
Virtual
Machine #n
Host Operating
System 1
Host Operating
System n
App AppApp
After Virtualization
Virtualization is a methodology of dividing the resources of a computer
hardware and software into multiple execution environments, known
as virtual machines.

16. Virtualization Benefits
Server Consolidation
Power Savings
Data Center Space Decrease
Ease of use
High Availability
Simplified Management
Improved Go-To-Market Time
Enhanced Security
Reduced Networking Costs
Reduced Carbon Footprint
Reduced TCO
Desktop Consolidation
Why People Love it
Remember: Business cares about results, not virtualization

17. Virtualization Challenges
Management
Problem Isolation
Charge-back and Ownership model
Licensing
Security and compliance
Training
17
Why People Hate it

18. Cloud Computing
neokobo.blogspot.com/2013/11/139-cloud-computing.html

19. Cloud Computing
Storage and Compute
happens here
Input and consumption of resulting information happens here
Cloud

20. Cloud Computing
• Computer scientist John McCarthy in the
1960s predicted that, "Computation may
someday be organized as a public utility.“
• What is cloud computing?
– In one of the more bare-bones definition, it is the
ability to process information on someone else’s
device.
• Cloud computing essentially transfers
computing tasks to the Internet.

21. “Cloud computing is a model for enabling
ubiquitous, convenient, on-demand
network access to a shared pool of
configurable computing resources (e.g.,
networks, servers, storage, applications,
and services) that can be rapidly
provisioned and released with minimal
management effort or service provider
interaction” -- NIST
The service is provided over
a network as opposed to
directly cabled. A
broadband or high-speed
network access is assumed.
There are many use cases
and many possible solution
for cloud computing, any
definition should be a
model that can be modified
to fit a particular use case.
The resources have a
management user interface
allowing the user to configure
and customize the resources
as needed
The asset or service can be
requested (provisioned) more
quickly than in the traditional
IT model. Usually in seconds
or minutes.
The service provider should be
able to “set it and forget it”. After
the initial deployment, ongoing
maintenance requirements
should be minimal.
This implies a self-service model.
The user has direct access to the
resources and can provision
(request) and release (return) an
asset with minimum overhead.
The relevant
compute
resources are
pooled and then
logically divided
and shared
amongst the
users. Related
terms include
multi-tenancy
and elastic.

22. Cloud Computing – Deployment
Models
Cloud Types Properties
Private Cloud For the exclusive use of a single organization.
Can have multiple consumers, e.g. business units
May exist on or off premise
Community
Cloud
For exclusive use of a community with a shared concern or purpose.
Owned and managed by the community and/or a third party.
May exist on or off premise
Public Cloud Available to the general public
Owned and managed by a service provider organization.
Exists on the premise of the service provider.
Hybrid Cloud Composition of two or more distinct cloud infrastructures.
Bound together by standard or proprietary technology yet otherwise
distinct.
Enables data and app portability

23. Big Data
www-03.ibm.com/ibm/history/exhibits/storage/storage_PH0350A.html
www.hoax-slayer.com/1956-hard-disk-drive.shtml
Two things we do not see anymore today:
1. Pan-Am Airlines
2. The 1956 5 MB, 305 RAMAC HDD
weighing approximately one ton

24. Big Data
“There are more things in heaven and earth,
Horatio, than are dreamt of in your philosophy.”
– Hamlet
From the beginning of recorded time until 2003,
we created 5 billion gigabytes of data. In 2011
the same amount was created every two days.
By 2013 that time will shrink to 10 minutes.
– The Human Face of Big Data – Rick Smolan & Jennifer Erwitt

25. SI Decimal Prefix Value
Hard Drive Storage
(decimal)
Processor/Virtual Storage
(binary)
Binary Digit 100 1 Bit 1 Bit
Byte 8 8 Bits 8 Bits
Kilobyte 103 1000 Bytes 1024 Bytes
Megabyte 106 1000 Kilobytes 1024 Kilobytes
Gigabyte 109 1000 Megabytes 1024 Megabytes
Terabyte 1012 1000 Gigabyte 1024 Gigabyte
Petabyte 1015 1000 Terabyte 1024 Terabyte
Exabyte 1018 1000 Petabyte 1024 Petabyte
Zettabyte 1021 1000 Exabyte 1024 Exabyte
Yottabyte 1024 1000 Zettabyte 1024 Zettabyte
Brontobyte 1027 1000 Yottabyte 1024 Yottabyte
Geopbyte 1030 1000 Brontobyte 1024 Brontobyte
Binary
Decision
Text
Character
Half Page
One Min.
MP3 Audio 894,784
Plaintext Pages
4,581,298
books 268,435,456
MP3 Files
245 Million
DVDs
375 Trillion
Digital
Pictures

26. What Is Big Data?
• Big data
– Too big in size, created too fast & with little or no
standard structure
– Structured + Semi-Structured + Unstructured Data
– Social media, sensors, retail, weather, etc.
– Difficult to process using traditional tools
• Big data spans three dimensions:
– Volume, Velocity and Variety.

27. The V’s of Big Data

28. Big Data Velocity Examples
• In one second on the internet there are…
– 197 Reddit votes cast
– 463 Instagram Photos posted
– 833 Tumblr posts posted
– 1024 Skype calls made
– 3935 Tweets tweeted
– 11574 Dropbox files uploaded
– 33,333 Google searches made
– 46,333 YouTube videos viewed
– 52,083 Facebook likes
Captured in 2014

29. Big Data
• Why Does Big data Matters?
– New Data
– Unlock Value
– Shape The Future
– Knowledge Is Power
• Data Sources
– Interconnectivity
– Machines
– Historical
Image from commons.wikimedia.org

30. Analyzing Big Data
• New Tools
– Data Scientist
– Scale-out Hardware
– Parallel Programming Algorithms
• MapReduce
• Hadoop
– Fast Storage:
• In-memory
• SSD
• DAS
– Languages for data mining/analytics: R, NoSQL,
Pig/Hive/Hadoop, C/C++, Perl,…

31. Criticizing Big Data
• Privacy
• “Truthiness”
• “Forest For The Tree”
• Decisions based on the past
• Correlation does not mean causation.
• “Big Judgment”
– Providing context for big data outcomes
…not everything that can be
counted counts, and not
everything that counts can be
counted. – William Bruce Cameron

32. Big Data Analytics
“In a world where every click can be tracked and
recorded, we shouldn’t be managing customers
by putting them into groups of similar people,
we really shouldn’t be guessing. We should be
able to read the signals that customers are
telling us to figure out what they want. I call
that personalization.”
– Nilan Peiris, CMTO HolidayExtras.com

33. Big Data
Known Knowns
• Things we know and we
know that we know
• working data
Known Unknowns
• Things we know that we
do NOT know
• data to be acquired
Unknown Knowns
• Things we do NOT know
that we know
• forgotten data
Unknown Unknowns
• Things we do NOT know
that we do NOT know
• data ignorance
Popularized partly by Donald Rumsfeld

34. data
engineer
Data Information Knowledge Understanding Wisdom
data
engineer
data
analyst
data
miner
data
scientist
raw data context experience models prediction
The Data Scientist can obtain actionable insight from an analysis of the data.
Big Data

35. Smart Dust
The Sensors That Track Every Thing, Everywhere
Sensors are ubiquitous, not just in devices

36. Thank You