The document discusses the size and growth of the Internet of Things market. It is projected to have a compound annual growth rate of 35% from 2013 to 2020. While consumer IoT devices will surpass commercial devices in the near term, commercial applications are expected to dominate the long-term IoT market. The document also outlines considerations for building an Internet of Everything system, including bridging information technology and operational technology, handling large volumes of data through edge computing, and providing analytics capabilities.

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It’s Huge, but How Huge?

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It’s Huge, but How Huge?
• IoT hype has surpassed Big Data
• Press has increased 10X in one year
• CAGR of IoT 2013-2020 is projected to be ~ 35%/year
• This is not a market
This is a collection of loosely related markets

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Is This the IoT Market?
• In # of devices:
• Commercial / industrial will pass consumer around 2018
• In $:
• Until 2020, consumer IoT will be greater
• Long term, commercial / industrial will be 2X consumer

6. “The network will play a crucial part in how we develop the car;
gathering data, learning from it and adapting will ultimately
determine our season” Christian Horner
Team Principal, Infiniti Red Bull Racing

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Edge
Devices
Data Computing Tiers
Optional Fog Nodes for Better Performance
Edge
Devices
Data Center
and
Applications
IoT Data System
Fog
Node
Fog
Node
Data Center
and
Applications
3 or More Tiered System2 Tiered System

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Basic Premises
Devices
send and receive data interacting with the
Network
where the data is transmitted, normalized, and filtered using
Edge Computing
before landing in
Data storage / Databases
accessible by
Applications
which process it and provide it to people who will
Act and Collaborate

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IoT World Forum Reference Model
Levels
Application
(Reporting, Analytics, Control)
Data Abstraction
(Aggregation & Access)
Data Accumulation
(Storage)
Edge Computing
(Data Element Analysis & Transformation)
Connectivity
(Communication & Processing Units)
Physical Devices & Controllers
(The “Things” in IoT)
Collaboration & Processes
(Involving People & Business Processes)
1
2
3
4
5
6
7
Sensors, Devices, Machines,
Intelligent Edge Nodes of all types
Center
Edge

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Internet of Things Design Issues
Sensors, Devices, Machines,
Intelligent Edge Nodes of all types
Center
Edge
IT
OT
Query
Based
Event
Based
Data at
Rest
Data in
Motion
Non-real
Time
Real
Time
Levels
1
2
3
4
5
6
7

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Bridging IT and OT
Sensors, Devices, Machines,
Intelligent Edge Nodes of all types
Center
Edge
Key Point:
IT – OT
IT
OT
Levels
1
2
3
4
5
6
7

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Bridging IT and OT: Introducing IoT “Edgeware”
Devices
Edge
Edge
computing
Device Control
• Configure (from the device provider)
• Status (from the device provider)
Device Interactions
• Discovery
• Addressing
• Protocol conversion
Middleware
• Listeners (Zigbee), brokers (MQTT)
• Event grouping / batch interactions
Data
• Normalize (standardize codes for the app)
• Filter (against pre-set criteria from the app)
• Expand (decode/expand cryptic codes)
• Aggregate (generate statistics)
• Notify/alert (to the app)
Combine the functions above
• Schedule (when to comm with the device)
• BPM (when multiple steps are needed)
Security
• Roles
• Privileges
An individual edge software
function may serve many
applications
Edge software can be
sourced completely
separately from the
vertical application

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Cisco IoE Sub-Systems
Data Transport
Data Collection
Data Leverage
Security

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Key Points:
• IT – OT
• Decoupling
Issue: Devices may
generate data faster than
apps can ingest it
Devices
Apps
Bridging IT and OT: Handling the Volume of Data
Sensors, Devices, Machines,
Intelligent Edge Nodes of all types
Center
Edge
Levels
1
2
3
4
5
6
7

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Edge
Devices
Application
IoT Data
System
Data is Queried as Needed
from the Application to the IDS
Data is Pushed Through IDS
as Generated from Edge
Devices to the Application
Edge
Devices
Application
IoT Data
System
Subscribe
Query
IoT Data System “Flow”

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Apps
Apps
BI
(reporting)
Apps Apps
Supervisory
Control
Systems
Data Transport
Data Collection
Data Leverage
IoE Consumers
Analytics

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Data Transformation

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Serving Reporting (BI) and Applications

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Apps Apps Apps
Data Transport
Data Collection
Data Leverage
IoE Consumers
Analytics

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IoE Analytics Introduces New Complexities
Key Issues:
• The velocity and volume of data may be huge
• In some cases, most of the data is unimportant
Traditional Capture
Data
Store
Data
Analyze
Data
Edge Computing
Becomes Crucial
IoT Analyze
Data
Store
Data
Capture Data
from Devices
Reduce Data
at the Edge

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Machine Data Meets Analytics

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Apps
Apps
BI
(reporting)
Apps Apps
Data Transport
Data Collection
Data Leverage
IoE Consumers
Analytics
Time
Series
Relational
streaming
fast
deep
Hadoop

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Requirements for an IoE Platform
• Reusable (modules)
• Framework (for edge processing and data transport)
• Options (fast data, deep data, streaming data)
• Flexible (for integration with IT)
• Easy (GUI tools)
• Completely open system (with multiple vendors)

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The Complete IoE System
Key Points:
• Bridging IT & OT
• Decoupling
- Scalability
- Agility
• Interoperability
• Legacy Compatibility
• Analytics
• Integrated with
the Enterprise
Sensors, Devices, Machines,
Intelligent Edge Nodes of all types
Center
Edge
Levels
1
2
3
4
5
6
7

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Example IoE System Design
Edge
Processing
Data Center
Fog
Processing
300 Platforms
IT Central
7,000 Oil Wells

26. Thank you.

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Requirements for an IoE Platform
• Reusable modules for collecting data from, and providing control to,
devices and machines
• Framework for edge processing
• Options for reliable transport of data through the system,
encompassing both batch and real-time streaming options
• Flexible mechanisms for integration with IT systems, reporting, and analytics
• Easy to use GUI tools to simplify development and deployment
for all aspects of the system
• A completely open system, where 3rd parties can provide devices, processing,
modules, analytics, applications or any combination

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