This document discusses potential fuel savings across various industries from implementing 1% efficiencies. It estimates $30 billion in savings for aviation, $66 billion for oil and gas, $63 billion for rail, $27 billion for healthcare, and $90 billion for power generation. It then outlines an Internet of Things architecture that collects data from devices and sensors, transmits it to a cloud platform for analysis, and delivers insights to users, devices, and applications. This enables alerts, automation, visibility, and informed decision making. Finally, it discusses common machine-to-machine communication patterns like telemetry, inquiries, commands, and notifications and considerations for implementing them at scale across millions of devices.

6. Note: Illustrative based on potential one percent savings applied across specific global industry sectors.
Source: GE estimates
Aviation
Oil & Gas
Rail
Healthcare
Power
Commercial
Gas-fired Generation
System-wide
Freight
Exploration & Development
1% Fuel Savings
1% Fuel Savings
1% Reduction in System Inefficiency
1% Reduction in System Inefficiency
1% Reduction in Capital Inefficiency
$30B
$66B
$63B
$27B
$90B

7. Alerts, Analytics,
Events and Access
Workflow and Business
Process AutomationThroughput and OEE
Visibility, Role-based
and Mobile
People Assets InformationProcess
Productive
environments
Sustainable
Performance
Reliable process
capabilities
Informed decision
making
• Familiar interaction
with natural user
interfaces
• Simple Role-
oriented workspace
• Collaborative
communication
• Connected
• Remote monitoring
• Business continuity
• Control
• Quality
• Standardization
• Flexibility
• Secure and timely
visibility
• Complete,
contextual, accurate
• Predictive and
actionable

9. Data is acquired from devices, sensors, applications and people.

10. Appropriate data is transmitted to a public or private
cloud.
The specific data transmitted, how it is transmitted,
and timing of transmission is determined by policy.
Policy includes considerations of “three Cs” – context,
connectivity, and cost.

11. Services are utilized to deliver the data, store it, and if
appropriate, initiate one or more associated data
pipelines.

12. Analytic pipelines perform analysis and generate
insight.
The resulting data delivers one of four things –
• Information for Subscribers
• Enhancements of Existing Data
• Recommend Action(s)
• Initiation of Action(s)

13. Insight is delivered to appropriate human,
device, and application audiences in the
forms of -
• Alerts/Notifications
• Reporting
• Command + Control
• Data Services
• Personalized User Experiences

15. Peer-to-Peer
Device-to-Service Service-to-Device
Machine-to-Machine communication is non-interactive, automated,
and bi-directional information exchange in operational systems,
performed between peers or between satellite systems and their
supporting backend services.

17. Common Activities and Composability
Peer-to-Peer
Device-to-Service Service-to-Device
Service-to-Service

18. Information Exchange Patterns
Telemetry
Information flowing
from a device to other
systems for conveying
status of device and
environment
Inquiries
Requests from devices
looking to gather
required information or
asking to initiate
activities
Commands
Commands from other
systems to a device or a
group of devices to
perform specific
activities
Notifications
Information flowing
from other systems to a
device (-group) for
conveying status
changes in the rest of
the world

19. Telemetry Types

20. Telemetry Considerations

21. Signal Characterization

22. Policy Considerations
The “three Cs” will help determine the appropriate telemetry to deliver at any given time.

23. Scale Unit
x10,000 devices
Data Analysis
Pipeline(s)
Gateway
Filtering and Aggregation
Routing
Control
System
Scale
Unit
Scale
Unit
Scale
Unit
x1,000,000 devices
Scale
Unit
DCBoundary
Device
Identity and
Metadata
Store
Provisioning
System
DataServices

24. Windows Azure Service Bus Messaging
Custom Protocol Gateway Host
MQTT CoAP … …
Telemetry/Request
Router
Notification/Command Router
Adapters Command API Host
Provisioning
Service
Device
Metadata
and Key
Store
HDInsight
BizTalkSv/Sr
Orleans
AzureStorage
SQL
SB
HTTP
HTTP
Devices
AMQP
1
2 3
4
Configs

26. fn

27. flt

28. Data Lake

32. Data Analysis
Pipeline(s)
DataServices
1 2
3
4
5
6
7

33. Project Passau
• Easy data exploration
through web-based
interface
• No programming required
• Flexible and extensible

34. Hadoop Options

35. Resulting Insight Types

39. Consuming Insight

44. Scale Unit
x10,000 devices
Data Analysis
Pipeline(s)
Gateway
Filtering and Aggregation
Routing
Control
System
Scale
Unit
Scale
Unit
Scale
Unit
x1,000,000 devices
Scale
Unit
DCBoundary
Device
Identity and
Metadata
Store
Provisioning
System
DataServices

45. PartitionMaster
Provisioning
API
Provisioning
Runtime
Partition Repo
Ingestion Topics Egress
Service Bus Standard Protocol Custom Protocol
Device Repo
Access Control
in0000 inFFFF…in0001 in0002
AMQPS HTTPS MQTT Custom Protocol Host
Protocol Adapters
diagall diagall diagall diagall
Telemetry Pump
N Instances
Telemetry
Adapter
Telemetry
Adapter
Telemetry
Adapter
Deployment
Runtime
out0000 outFFFF…out0001 out0002
s0001
s0002
s03E7
s0001
s0002
s03E7
s0001
s0002
s03E7
s0001
s0002
s03E7
g0000/
rte0000
g0001/
rte0000
g0000/
rte0001
g0001/
rte0001
out0
out1
out2
out0
out1
out2
out0
out1
out2
out0
out1
out2
n Groups of m Routers
SB
AMQPS

46. HDInsight
Azure Blob
Storage (ASV)
Azure SQL DB
Client Data
Services
(Worker Role)